Search

The analysis of textures is an important task in image processing and computer vision because it provides significant data for image retrieval, synthesis, segmentation, and classification. Automatic texture recognition is difficult, however, and necessitates advanced computational techniques due to the complexity and diversity of natural textures. This paper presents a method for classifying textures using graphs; specifically, natural and horizontal visibility graphs. The related image natural visibility graph (INVG) and image horizontal visibility graph (IHVG) are used to obtain features for classifying textures. These features are the clustering coefficient and the degree distribution. The suggested outcomes show that the aforementioned technique outperforms traditional ones and even comes close to matching the performance of convolutional neural networks (CNNs). Classifiers such as the support vector machine (SVM), K-nearest neighbor (KNN), decision tree (DT), and random forest (RF) are utilized for the categorization. The suggested method is tested on well-known image datasets like the Brodatz texture and the Salzburg texture image (STex) datasets. The results are positive, showing the potential of graph methods for texture classification. [ABSTRACT FROM AUTHOR]

As one of fundamental texture classification methods, LBP-based descriptors have attracted considerable attention due to the efficiency, simplicity, and high performance. However, most of binary pattern methods cannot effectively capture the texture information with scale changes. Inspired by this, this paper proposes a multi-scale threshold integration encoding strategy for texture classification. The essence of this strategy is to introduce the multi-scale local texture information in the view of thresholding. Based on this, we propose the local multi-scale center pattern, local multi-scale sign pattern, and local multi-scale magnitude pattern to extract and describe the multi-scale local texture information. Then, the three sub-patterns are jointly combined to generate the final descriptor for texture classification tasks. The experimental results on three popular texture databases significantly demonstrate that the proposed texture descriptor is very discriminative and powerful for visual texture classification tasks. [ABSTRACT FROM AUTHOR]

Our world consists not only of objects and scenes but also of materials of various kinds. Being able to recognize the materials that surround us (e.g., plastic, glass, concrete) is important for humans as well as for computer vision systems. Unfortunately, materials have received little attention in the visual recognition literature, and very few computer vision systems have been designed specifically to recognize materials. In this paper, we present a system for recognizing material categories from single images. We propose a set of low and mid-level image features that are based on studies of human material recognition, and we combine these features using an SVM classifier. Our system outperforms a state-of-the-art system (Varma and Zisserman, TPAMI 31(11):2032-2047, ) on a challenging database of real-world material categories (Sharan et al., J Vis 9(8):784-784a, ). When the performance of our system is compared directly to that of human observers, humans outperform our system quite easily. However, when we account for the local nature of our image features and the surface properties they measure (e.g., color, texture, local shape), our system rivals human performance. We suggest that future progress in material recognition will come from: (1) a deeper understanding of the role of non-local surface properties (e.g., extended highlights, object identity); and (2) efforts to model such non-local surface properties in images. [ABSTRACT FROM AUTHOR]

In this paper, a new formula is proposed that uses local entropy for texture feature extraction. This new method is similar to entropy; however, it calculates the local entropy of each local patch of textures. Entropy (ENT) is an attribute that measures the randomness of gray-level distribution of image. Entropy extracts dissimilarity of each local patch. In this paper, local entropy is compared to Local Binary Pattern (LBP) and local variance (VAR). All of these descriptors are rotation invariant and are used for extracting the features from each local neighborhood of textures. In spite of low accuracy of VAR and LBP the performance of ENT does not decrease significantly for noisy textures. In other words, ENT is more robust to noise than VAR and LBP. Implementations on Outex, UIUC, CUReT and MeasTex datasets show that entropy is more accurate than variance and LBP. Similar to VAR and LBP, ENT can be combined with other descriptors to improve the performance of classification. For almost all datasets that are used in implementation part, LBP/ENT is more accurate than LBP/VAR for normal and noisy textures. Also the ENT accuracy outperforms the accuracy of VAR and LBP and most of the advanced noise robust LBP versions for low Signal to Noise Ratio (SNR) values (SNR < 10). ENT feature is a continuous value so it is necessary to quantize to discrete value for histogram. The quantization and train step of ENT is the same as VAR. [ABSTRACT FROM AUTHOR]

Skin cancer is a most dangerous type of cancer found in humans. It is found in various types such as melanoma, basal cell carcinoma and squamous cell carcinoma. Among others, Melanoma is the most serious and dangerous cancer which leads from a simple skin mark to a tumour. Early detection of the type of skin cancer can helpsin better cure. In this paper, a new method is proposed for the detection of type of skin cancer. The proposed method integrates the features of DRLBP (Dominant rotated local binary pattern) and MRELBP (median robust extended local binary pattern) methods for feature extraction of the skin cancer lesions. The support vector machine (SVM) classifier is then used to classify the images based on the calculated features. To evaluate the performance of the proposed method, skin cancer images containing 367 lesions are used from ISICstandard dataset from the results, it is analysed that the proposed method can extracts microstructure and macrostructure texture information. Furthermore, it is robust to the rotation variation.It is also observed that the proposed method gives better results than the other state of the art local binary pattern based feature extraction methods. [ABSTRACT FROM AUTHOR]

Multi-scale analysis has been widely used for constructing texture descriptors by modeling the coefficients in transformed domains. However, the resulting descriptors are not robust to the rotated textures when performing texture classification. To alleviate this problem, we in this paper propose a multi-scale counting and difference representation (CDR) of image textures for texture classification. Particularly, we first extract a single-scale CDR feature consisting of the local counting vector (LCV) and the differential excitation vector (DEV). The LCV is established to capture different types of textural structures using the discrete local counting projection, while the DEV is used to describe the difference information of textures in accordance with the differential excitation projection. Finally, the multi-scale CDR feature of a texture image is constructed by combining CDRs at different scales. Experimental results on Brodatz, VisTex, and Outex databases demonstrate that our proposed multi-scale CDR-based texture classification method outperforms five representative texture classification methods. [ABSTRACT FROM AUTHOR]

Texture classification is widely used in understanding the visual patterns and has wide range of applications. The present paper derived a novel approach to classify the stone textures based on the patterns occurrence on each sub window. The present approach identifies overlapped nine 5 bit T-patterns (O5TP) on each 5?5 sub window stone image. Based the number of occurrence of T-patterns count the present paper classify the stone images into any of the four classes i.e. brick, granite, marble and mosaic stone images. The novelty of the present approach is that no standard classification algorithm is used for the classification of stone images. The proposed method is experimented on Mayang texture images, Brodatz textures, Paul Bourke color images, VisTex database, Google color stone texture images and also original photo images taken by digital camera. The outcome of the results indicates that the proposed approach percentage of grouping performance is higher to that of many existing approaches. [ABSTRACT FROM AUTHOR]

This paper deals with multi-color space texture classification. Two approaches are proposed and compared: a multi-color space histogram selection (MCSHS) and a multi-color space bin selection. These approaches select local binary pattern (LBP) histograms or LBP bins that have been processed from images coded in multiple color spaces. On the one hand, the proposed LBP-based feature selection scheme overcomes the difficulty of choosing a relevant color space, and on the other hand, it takes advantage of the specific properties of several color spaces by combining them. Experiments show that the MCSHS approach is relevant for color texture classification issues that require good performances whether in accuracy or classification computation time. [ABSTRACT FROM AUTHOR]

Multi-view texture classification is a very challenging task since the view-point variation often leads to the inconsistent local texton patterns. Existing studies focus on the extraction of scale, rotation or affine invariant representations by some specially designed invariant measurements or local descriptors. Differently, in this paper, we propose another framework for multi-view texture classification. A number of synthetic images are hierarchically created to enlarge the training dataset to cover the possible variations of different view-points. Then, a classifier based on random forests is trained based on these synthetic images. In the classification stage, we also create synthetic images for each testing image, and the synthetic images are classified with the pre-trained classifier. The final decision for this testing image is made by the majority voting of the classification results of all these synthetic images. The classification performance is evaluated on the UIUC texture dataset. Our method achieves the classification rate of 99.21 %, which is higher than most of the state-of-the-arts. [ABSTRACT FROM AUTHOR]

In this paper, four novel, simple and robust approaches, which are left to right local binary patterns (LBPLL2R), top to down local binary patterns (LBPT2D), cube surface local binary pattern (LBPSurfaces), and cube diagonal local binary pattern (LBPDiagonal), were proposed in order to exact texture features in color images. These approaches were based on the local binary pattern (LBP), which is an effective statistical texture descriptor and can be employed in gray images. Proposed approaches were evaluated and validated in four datasets, which are Outex, KTH_TIPS, KTH_TIPS2, and USPtex datasets. The images in these datasets are in RGB, HSV, YIQ, and YCbCr color formats. Achieved results by these approaches were compared with the obtained results by the classical LBP and literature findings. As a result, the proposed approaches performed better than the traditional LBP method and they found effective in the classification of color texture images, especially in images, which are in RGB and HSV formats. Furthermore, noise robustness and time complexity of the proposed approaches were validated. [ABSTRACT FROM AUTHOR]

This paper presents a simple, efficient, yet robust approach, named joint-scale local binary pattern (JLBP), for texture classification. In the proposed approach, the joint-scale strategy is developed firstly, and the neighborhoods of different scales are fused together by a simple arithmetic operation. And then, the descriptor is extracted from the mutual integration of the local patches based on the conventional local binary pattern (LBP). The proposed scheme can not only describe the micro-textures of a local structure, but also the macro-textures of a larger area because of the joint of multiple scales. Further, motivated by the completed local binary pattern (CLBP) scheme, the completed JLBP (CJLBP) is presented to enhance its power. The proposed descriptor is evaluated in relation to other recent LBP-based patterns and non-LBP methods on popular benchmark texture databases, Outex, CURet and UIUC. Generally, the experimental results show that the new method performs better than the state-of-the-art techniques. [ABSTRACT FROM AUTHOR]

Purpose – The purpose of this paper is to review and provide a detailed performance evaluation of a number of texture descriptors that analyse texture at micro-level such as local binary patterns (LBP) and a number of standard filtering techniques that sample the texture information using either a bank of isotropic filters or Gabor filters. Design/methodology/approach – The experimental tests were conducted on standard databases where the classification results are obtained for single and multiple texture orientations. The authors also analysed the performance of standard filtering texture analysis techniques (such as those based of LM and MR8 filter banks) when applied to the classification of texture images contained in standard Outex and Brodatz databases. Findings – The most important finding resulting from this study is that although the LBP/C and the multi-channel Gabor filtering techniques approach texture analysis from a different theoretical perspective, in this paper the authors have experimentally demonstrated that they share some common properties in regard to the way they sample the macro and micro properties of the texture. Practical implications – Texture is a fundamental property of digital images and the development of robust image descriptors plays a crucial role in the process of image segmentation and scene understanding. Originality/value – This paper contrast, from a practical and theoretical standpoint, the LBP and representative multi-channel texture analysis approaches and a substantial number of experimental results were provided to evaluate their performance when applied to standard texture databases. [ABSTRACT FROM AUTHOR]

This paper proposes an effective scale- and rotation-invariant local binary pattern (LBP) feature for texture classification. A circular neighboring set of an image pixel is defined as a scale-adaptive texton by taking into account the fundamental local structure property of the pixel. The scale space of a texture image is derived by the Laplacian of the Gaussian and then employed to determine the optimal scale of each pixel reflecting the characteristic length of the corresponding structure and determining the radius of the scale-adaptive texton. Different pixels have different optimal scales, resulting in the scale invariance. Contrary to the traditional LBP features that usually ignore global spatial information, the proposed method also defines subuniform patterns of each uniform pattern to improve the discrimination. For each uniform pattern, the subuniform pattern with the maximum statistical value is defined as the dominant orientation subuniform pattern. It is moved to the first column, and the others are circularly shifted. Experimental results demonstrate a good discrimination capability of the proposed scale- and rotation-invariant LBP in texture classification. Particularly, the LBP based on the scale-adaptive texton is promising to be powerful for texture description and scale-invariant texture classification, and the circular shift subuniform LBP can further improve the performance in the rotation-invariant texture classification. [ABSTRACT FROM AUTHOR]

This paper proposes a block-based selection random forest (BBSRF) for texture classification task using multi-fractal spectrum (MFS) feature descriptor. The random feature selection method for node splitting in random forest may omit some features which would be informative and critical to represent the instances. The BBSRF ensures that each feature would be considered via the block-based selection strategy. In BBSRF, all features are divided into $$k$$ blocks; next, we generate synthesis feature subset which is made up of all features in one block and $$m$$ random features from the remaining $$(k-1)$$ blocks; finally, each node splitting of the random tree is operated on one synthesis feature subset. After all blocks have been searched, all features are re-divided into new $$k$$ blocks. The above process works iteratively until the satisfactory result is obtained. Once the random trees have been built, a testing instance is classified by voting from them. We conducted the experiments on five texture benchmark datasets with the help of MFS feature. Experimental results demonstrate the excellent performance of the proposed method in comparison with state-of-the-art results on these datasets. [ABSTRACT FROM AUTHOR]

Texture image classification is important in computer vision research. To effectively capture texture patterns, a distinctive feature such as a local binary pattern (LBP) is needed. An LBP is robust against monotonic and gray-scale variations and it computes quickly. Its robustness and speed advantage have made it popular in various texture analysis applications. However, an LBP is sensitive to noise, particularly smooth weak illumination gradients in near-uniform regions. To mitigate the effect of noise and increase distinctiveness, a local ternary pattern (LTP) is proposed. Compared with a binary coding LBP, an LTP adopts ternary coding. As a result, an LTP can better tolerate noise and is significantly more distinctive. These advantages of an LTP effectively improve its classification accuracy. However, the potential of ternary coding is not fully explored in LTPs because a ternary pattern is split into a pair of binary patterns. In this paper, to fully explore the distinctiveness in the local pattern, the feature extraction process is formulated as an integer decomposition problem, which is a generalized version of the Bachet de Meziriac weight problem (BMWP). Following this generalization, a local $n$ -ary pattern (LNP) is proposed, for which the LBP is a special case parametrized under $n=2$ . The LTP is not a special case of the LNP. Both LBP and LTP are used as benchmark methods to evaluate LNPs performance due to their well-recognized success. In addition, a rotation-invariant and uniform LNP is also proposed and compared with a rotation-invariant and uniform LBP. The proposed LNP achieves significantly improved texture classification accuracy compared with the LBP and also demonstrates considerable improvement over the LTP. [ABSTRACT FROM PUBLISHER]

Highlights • The paper presents a novel central pixel selection (CPS) strategy for LBP framework. • The proposed strategy can be applied to LBP and its variants. • The results of proposed CPS strategy are significant. Abstract Local binary pattern (LBP) has been successfully used in computer vision and pattern recognition applications, such as biomedical image analysis, remote sensing and image retrieval. However, the current LBP-based features, which assign a fixed sampling radius for all pixels in a single scale, completely ignore the fact that different central pixels actually have different local gray-value distributions and the proper sampling radius should be different for pixels. In this paper, we propose a novel and effective central pixel selection (CPS) strategy by using gradient information to classify central pixels of a texture image into different classes based on their local gray-value distributions. Then, we introduce this CPS strategy into the LBP framework and assign an adaptive sampling radius for each central pixel according to the class it belongs to. As a preprocessing step of LBP framework, this CPS strategy can also be integrated into any other LBP variants so as to extract more effective local texture features. Extensive experiments on five representative texture databases of Outex, UIUC, CUReT, UMD and ALOT validate the efficiency of the proposed central pixel selection (CPS) strategy, which can achieve almost 16% improvement over the original LBP and 1%–10% improvement compared with the best classification accuracy among other benchmarked state-of-the-art LBP variants. [ABSTRACT FROM AUTHOR]

Texture is a fundamental characteristic of many types of images, and texture representation is one of the essential and challenging problems in computer vision and pattern recognition which has attracted extensive research attention over several decades. Since 2000, texture representations based on Bag of Words and on Convolutional Neural Networks have been extensively studied with impressive performance. Given this period of remarkable evolution, this paper aims to present a comprehensive survey of advances in texture representation over the last two decades. More than 250 major publications are cited in this survey covering different aspects of the research, including benchmark datasets and state of the art results. In retrospect of what has been achieved so far, the survey discusses open challenges and directions for future research. [ABSTRACT FROM AUTHOR]

Recently, the local binary patterns (LBP) have been widely used in the texture classification. The LBP methods obtain the binary pattern by comparing the gray scales of pixels on a small circular region with the gray scale of their central pixel. The conventional LBP methods only describe microstructures of texture images, such as edges, corners, spots and so on, although many of them show good performances on the texture classification. This situation still could not be changed, even though the multi-resolution analysis technique is adopted by LBP methods. Moreover, the circular sampling region limits the ability of the conventional LBP methods in describing anisotropic features. In this paper, we change the shape of sampling region and get an extended LBP operator. And a multi-structure local binary pattern (Ms-LBP) operator is achieved by executing the extended LBP operator on different layers of an image pyramid. Thus, the proposed method is simple yet efficient to describe four types of structures: isotropic microstructure, isotropic macrostructure, anisotropic microstructure and anisotropic macrostructure. We demonstrate the performance of our method on two public texture databases: the Outex and the CUReT. The experimental results show the advantages of the proposed method. [ABSTRACT FROM AUTHOR]

The autocorrelation is often used in signal processing as a tool for finding repeating patterns in a signal. In image processing, there are various image analysis techniques that use the autocorrelation of an image in a broad range of applications from texture analysis to grain density estimation. This paper provides an extensive review of two recently introduced and related frameworks for image representation based on autocorrelation, namely Patch Autocorrelation Features (PAF) and Translation and Rotation Invariant Patch Autocorrelation Features (TRIPAF). The PAF approach stores a set of features obtained by comparing pairs of patches from an image. More precisely, each feature is the euclidean distance between a particular pair of patches. The proposed approach is successfully evaluated in a series of handwritten digit recognition experiments on the popular MNIST data set. However, the PAF approach has limited applications, because it is not invariant to affine transformations. More recently, the PAF approach was extended to become invariant to image transformations, including (but not limited to) translation and rotation changes. In the TRIPAF framework, several features are extracted from each image patch. Based on these features, a vector of similarity values is computed between each pair of patches. Then, the similarity vectors are clustered together such that the spatial offset between the patches of each pair is roughly the same. Finally, the mean and the standard deviation of each similarity value are computed for each group of similarity vectors. These statistics are concatenated to obtain the TRIPAF feature vector. The TRIPAF vector essentially records information about the repeating patterns within an image at various spatial offsets. After presenting the two approaches, several optical character recognition and texture classification experiments are conducted to evaluate the two approaches. Results are reported on the MNIST (98.93%), the Brodatz (96.51%), and the UIUCTex (98.31%) data sets. Both PAF and TRIPAF are fast to compute and produce compact representations in practice, while reaching accuracy levels similar to other state-of-the-art methods. [ABSTRACT FROM AUTHOR]

The quality of olive fruit and its virgin olive oil is a main concern for consumers and fruit industrial companies. The effectiveness and fast detection of olive's skin defects is the most decisive factor in determining its quality. It is necessary to design and implement image processing tools for segmentation and correct classification of the different fresh incoming olive batches. In this paper, we propose a new automatic image segmentation algorithm, based on discrete wavelets transform. The aim of the segmentation algorithm is to discriminate between olives and the background with the challenge of irregular and dispersive lesion borders, low contrast, artifacts in the olive fruit and variety of colors within the interest region. The second part of our work proposes a scheme for olive fruit classification. The classifier first identifies the olive fruit color and then, based upon discrete wavelets transform and Tamura statistical texture features, the healthy olive fruit is distinguished from the damaged one. The new texture feature vector is, then, compared with the robust Local Binary Pattern feature vector. The simplicity of our segmentation and classification algorithms makes them appropriate for designing a productive and profitable computer vision machine. [ABSTRACT FROM AUTHOR]

This paper presents a color-texture descriptor based on the local mapped pattern approach for color-texture classification under different lighting conditions. The proposed descriptor, namely extended color local mapped pattern (ECLMP), considers the magnitude of the color vectors inside the RGB cube to extract color-texture information from the images. These features are combined with texture information from the luminance image in a multiresolution fashion to get the ECLMP feature vector. The robustness of the proposed method is evaluated using the RawFooT, KTH-TIPS-2b, and USPtex databases. The experimental results show that the proposed descriptor is more robust to changes in the illumination condition than 22 alternative commonly used descriptors. [ABSTRACT FROM AUTHOR]

Texture classification has a long history in computer vision. In the last decade, the strong affirmation of deep learning techniques in general, and of convolutional neural networks (CNN) in particular, has allowed for a drastic improvement in the accuracy of texture recognition systems. However, their performance may be dampened by the fact that texture images are often characterized by color distributions that are unusual with respect to those seen by the networks during their training. In this paper we will show how suitable color balancing models allow for a significant improvement in the accuracy in recognizing textures for many CNN architectures. The feasibility of our approach is demonstrated by the experimental results obtained on the RawFooT dataset, which includes texture images acquired under several different lighting conditions. [ABSTRACT FROM AUTHOR]

In this paper, the task of texture image classification is analyzed by using Discrete Wavelet Transform (DWT) and Discrete Curvelet Transform (DCT). The wavelet and curvelet coefficients are used to describe the textures in the given image. These coefficients are obtained by the decomposition process. First, the texture image is decomposed by using DWT and DCT at multiscale. As the sub-bands in the decomposed image carries diverse information about the texture, predefined number of coefficients is selected in each sub-band image. Before selecting the coefficients, sub-band coefficients are sorted in order to account high energy coefficients. The results show that the classification accuracy of DWT based features outperforms the DCT energies. The classification accuracy of DWT is 5% higher than DCT features at 2-level decomposition with 50% of coefficients used. [ABSTRACT FROM AUTHOR]

Texture classification is a challenging and important problem in image analysis. graphical models (GM) are promising tools for texture analysis. In this paper, we address the problem of learning the structure of Gaussian graphical models (GGM) for texture models. GGM can be considered as regression problems due to the connection between the local Markov properties and conditional regression of a Gaussian random variable. We utilize L1-penalty regularization technique for appropriate neighborhood selection and parameter estimation simultaneously. The proposed algorithms are applied in texture synthesis and classification. Experimental results on Brodatz textures demonstrate that the proposed algorithms have good performance and prospects. [ABSTRACT FROM AUTHOR]

Local Binary Patterns (LBP) have emerged as one of the most prominent and widely studied local texture descriptors. Truly a large number of LBP variants has been proposed, to the point that it can become overwhelming to grasp their respective strengths and weaknesses, and there is a need for a comprehensive study regarding the prominent LBP-related strategies. New types of descriptors based on multistage convolutional networks and deep learning have also emerged. In different papers the performance comparison of the proposed methods to earlier approaches is mainly done with some well-known texture datasets, with differing classifiers and testing protocols, and often not using the best sets of parameter values and multiple scales for the comparative methods. Very important aspects such as computational complexity and effects of poor image quality are often neglected. In this paper, we provide a systematic review of current LBP variants and propose a taxonomy to more clearly group the prominent alternatives. Merits and demerits of the various LBP features and their underlying connections are also analyzed. We perform a large scale performance evaluation for texture classification, empirically assessing forty texture features including thirty two recent most promising LBP variants and eight non-LBP descriptors based on deep convolutional networks on thirteen widely-used texture datasets. The experiments are designed to measure their robustness against different classification challenges, including changes in rotation, scale, illumination, viewpoint, number of classes, different types of image degradation, and computational complexity. The best overall performance is obtained for the Median Robust Extended Local Binary Pattern (MRELBP) feature. For textures with very large appearance variations, Fisher vector pooling of deep Convolutional Neural Networks is clearly the best, but at the cost of very high computational complexity. The sensitivity to image degradations and computational complexity are among the key problems for most of the methods considered. [ABSTRACT FROM AUTHOR]

This paper proposes a simple yet effective novel classifier fusion strategy for multi-class texture classification. The resulting classification framework is named as Classification Confidence-based Multiple Classifier Approach (CCMCA). The proposed training based scheme fuses the decisions of two base classifiers (those constitute the classifier ensemble) using their classification confidence to enhance the final classification accuracy. 4-fold cross validation approach is followed to perform experiments on four different texture databases those vary in terms of orientation, number of texture classes and complexity. Apart from its simplicity, the proposed CCMCA method shows better and consistent performance with lowest standard deviation as compared to fixed rule and simple trainable fusion techniques irrespective of the feature set used across all the databases used in the experiment. The performance gain of the proposed CCMCA method over other competing methods is found to be statistically significant. [ABSTRACT FROM AUTHOR]

This paper proposed a new method based on spatial filter banks and discrete wavelet transform (DWT) for invariant texture classification. The method used a multi-resolution analysis method like DWT and applied the proposed filter bank on different resolutions. Then, a simple fusion of features on different resolutions was used for invariant texture analysis. A comprehensive study was done to examine the effectiveness of the proposed method. Different datasets with different properties were used in this paper such as Brodatz, Outex, and KTH-TIPS for the evaluation. Local binary pattern (LBP) methods have been one of the powerful methods in recent years for invariant texture classification. A comparative study was performed with some state-of-the-art LBP methods. This comparison indicated promising results for the proposed approach as compared with the LBP methods. [ABSTRACT FROM AUTHOR]

This paper addresses the challenging problem of the recognition and classification of textured surfaces given a single instance acquired under unknown pose, scale and illumination conditions. We propose a novel texture descriptor, the Adaptive Median Binary Pattern (AMBP) based on an adaptive analysis window of local patterns. The principal idea of the AMBP is to convert a small local image patch to a binary pattern using adaptive threshold selection that switches between the central pixel value as used in the Local Binary Pattern (LBP) and the median as in Median Binary Pattern (MBP), but within a variable sized analysis window depending on the local microstructure of the texture. The variability of the local adaptive window is included as joint information to increase the discriminative properties. A new multiscale scheme is also proposed in this paper to handle the texture resolution problem. AMBP is evaluated in relation to other recent binary pattern techniques and many other texture analysis methods on three large texture corpora with and without noise added, CUReT, Outex_TC00012 and KTH_TIPS2. Generally, the proposed method performs better than the best state-of-the-art techniques in the noiseless case and significantly outperforms all of them in the presence of impulse noise. [ABSTRACT FROM AUTHOR]

In this paper we propose a new rotation invariant feature descriptor for texture classification and clustering via first identifying the so-called principal direction of textures with the well-known Radon transform and then extracting features defined by the fractional Fourier transform of different order from the rotated textures along their principal direction. The performance of the proposed method is evaluated using different kind of texture sets. Results show the advantage of the proposed method over some existing algorithms. [ABSTRACT FROM AUTHOR]

As a newly developed singular value decomposition of the reduced quaternion matrix (SVDRQ), the two reduced quaternion unitary matrices can effectively capture the intrinsic geometric structures and smooth contours of color texture image. The projection vector by the two unitary matrices is used as dominant features for color texture classification. In this paper, we proposed new algorithm to implement the computation of the SVDRQ, and then proposed new color texture classification scheme based on SVDRQ, the Euclidean distance is applied as classifier in the proposed scheme. It is demonstrated by the experiments that our proposed scheme significantly improves the color texture classification accuracy in comparison with several conventional texture classification approaches. [ABSTRACT FROM AUTHOR]

Texture descriptors have been quite popular in medical image analysis, particularly in histopathologic images (HIs), due to the variability of the texture found in such images and the tissue appearance due to irregularity in the staining process. Such variability may exist depending on differences in the staining protocol, such as fixation, inconsistency in the staining condition, and reagents, either between laboratories or in the same laboratory. However, extracting texture features for quantifying HI information in a discriminant way is challenging, given that the distribution of intrinsic properties of such images forms a non-deterministic complex system. This paper proposes a novel method that quantifies such intrinsic properties using ecological diversity measures and discrete wavelet transform. The experimental results on two HI datasets have shown that the proposed method overcomes state-of-the-art shallow and deep methods • Information-theoretical measure of ecological diversity for texture characterization. • Exploitation of independent wavelet sub-band coefficients non-linear interactions. • Unexplored fusion of wavelet features and statistical properties of taxonomic indices. • Texture characterization across HIs with promising results for real-world datasets. [ABSTRACT FROM AUTHOR]

A natural object of study in texture representation and material classification is the probability density function, in pixel-value space, underlying the set of small patches from the given image. Inspired by the fact that small $$n\times n$$ high-contrast patches from natural images in gray-scale accumulate with high density around a surface $$\fancyscript{K}\subset {\mathbb {R}}^{n^2}$$ with the topology of a Klein bottle (Carlsson et al. International Journal of Computer Vision 76(1):1-12, ), we present in this paper a novel framework for the estimation and representation of distributions around $$\fancyscript{K}$$ , of patches from texture images. More specifically, we show that most $$n\times n$$ patches from a given image can be projected onto $$\fancyscript{K}$$ yielding a finite sample $$S\subset \fancyscript{K}$$ , whose underlying probability density function can be represented in terms of Fourier-like coefficients, which in turn, can be estimated from $$S$$ . We show that image rotation acts as a linear transformation at the level of the estimated coefficients, and use this to define a multi-scale rotation-invariant descriptor. We test it by classifying the materials in three popular data sets: The CUReT, UIUCTex and KTH-TIPS texture databases. [ABSTRACT FROM AUTHOR]

Texture analysis and classification are some of the problems which have been paid much attention by image processing scientists since late 80s. If texture classification is done accurately, it can be used in many cases such as object tracking, visual pattern recognition, and face recognition. Since now, so many methods are offered to solve this problem. Against their technical differences, all of them used same popular databases to evaluate their performance such as Brodatz or Outex which may be make their performance biased on popular databases. In this paper, some metrics are proposed to collect more actual database of texture images. The proposed approach is included two stages. The first one is developing feature representation based on structural and statistical features and the next one is consisted an algorithm which termed SFCO to collect images based on metrics. Finally, in order to prove the quality of developed metrics, some texture classification approaches are applied on proposed database. Decreasing the probability of biasing texture classification approaches on popular database is the main advantage of this paper. [ABSTRACT FROM AUTHOR]

Abstract: This paper presents a novel, simple, yet powerful and robust method for rotation invariant texture classification. Like the Local Binary Patterns (LBP), the proposed method considers at each pixel a neighboring function defined on a circle of radius R. We define local frequency components as the magnitude of the coefficients of the 1D Fourier transform of the neighboring function. By applying different bandpass filters on the 2D Fourier transform of the local frequency components, we define our Local Frequency Descriptors (LFD). The LFD features are added dynamically from low frequencies to high. The features defined in this paper are invariant to rotation. As well, they are robust to noise. The experimental results on the Outex, CUReT, and KTH-TIPS datasets show that the proposed method outperforms state-of-the-art texture analysis methods. The results also show that the proposed method is very robust to noise. [Copyright &y& Elsevier]

The color of pixels can be represented in different color spaces which take into account different properties. However, no color space is well-suited to the discrimination of all texture databases and the prior determination of such a space is not easy. In this paper, we compare the performances reached by two texture classification schemes that use color spaces: (a) the single color space selection approach, that defines a set of texture features and then selects the color space with which the texture features allow to reach the highest classification accuracy, (b) the multi-color space feature selection (MCSFS) approach, that selects texture features which have been processed from images coded into different color spaces. Experiments carried out with benchmark texture databases show that taking advantage simultaneously of the properties of several color spaces thanks to the MCSFS approach improves the rates of well-classified images with lower learning and decision processing times. [ABSTRACT FROM AUTHOR]

Abstract Aiming at the defect of Local Binary Pattern (LBP) and its variants, this paper presents a new modeling of the conventional LBP operator for texture classification. Named Attractive-and-Repulsive Center-Symmetric Local Binary Patterns (ACS-LBP and RCS-LBP), the proposed new texture descriptors preserve the advantageous characteristics of uniform LBP. Based on local attractive-and-repulsive characteristics, the proposed local texture modeling can really inherit good properties from both gradient and texture operators than the Center-Symmetric Local Binary Patterns (CS-LBP) does. Different from CS-LBP , which considers four doublets around the center pixel, the proposed methods take into account the four triplets corresponding to the vertical and horizontal directions, and the two diagonal directions by including the value of the central pixel in the modeling. In addition, Average Local Gray Level (ALGL), Average Global Gray Level (AGGL) and the median value over 3 × 3 neighborhood are introduced to capture both microstructure and macrostructure texture information. To capture the coarse and fine information of the features and thus to make ACS-LBP and RCS-LBP more robust and stable, multiscale ARCS-LBP descriptor is proposed. There is no necessity to learn texton dictionary, as in methods based on clustering, and no tuning of parameters is required to deal with different datasets. Extensive experiments performed on thirteen challenging representative texture databases show that the proposed operators can achieve impressive classification accuracy. Furthermore, we clearly validate the feasibility of the proposed ACS-LBP , RCS-LBP and ARCS-LBP descriptors by comparing their results with those obtained with a large number of recent state-of-the-art texture descriptors including deep features. Statistical significance of achieved accuracy improvement is demonstrated through Wilcoxon signed rank test. Highlights • We introduce a formal definition of Attractive-and-Repulsive Binary Thresholding Functions. • Two new powerful descriptors: Attractive and Repulsive Center-Symmetric Local Binary Patterns are proposed. • Multi-scale feature is incorporated by concatenating ACS-LBP and RCS-LBP into a single vector feature. • Extensive evaluation on 13 challenging representative texture databases is performed. • 72 recent state-of-the-art LBP variants as well as 3 deep learning features are evaluated. [ABSTRACT FROM AUTHOR]

Texture feature extraction is an important task in image processing and computer vision. Typical applications include automated inspection, image retrieval or medical analysis. In this paper we propose a noise robust and rotation invariant approach to texture feature analysis and classification. The proposed framework is based on a simple texture feature extraction step which decomposes the image structures by means of a family of orientated filters. The output is an orientation density vector which is called Orientation Feature Vector (OFV). The OFV can be used as the input feature vector to a texture classifier, and in addition, by using an interpolation step it is possible to extract the main orientations of the texture from the OFD, providing additional high level features for image analysis. In this work, three families of filters have been studied in the texture feature extraction step. The experimental results show the ability of the proposed framework in classification problems, improving more than 20% the results of other state-of-the-art methods when a high level of Gaussian noise is considered. [ABSTRACT FROM AUTHOR]

In this paper, the three level descriptions from LTP and the directional features from LDP are combined to form a new local feature descriptor, referred to as local directional ternary pattern (LDTP) for texture classification. LDTP is a framework, which consists in encoding both contrast information and directional pattern features in a compact way based on local derivative variations. To achieve robustness, the proposed operator first computes for each pixel within its 3 × 3 overlapping grayscale image patch, on the one hand, eight directional edge responses using the eight Frei–Chen masks, and on the other hand, central edge response through the 2nd derivative of Gaussian filter to capture more detailed information. This allows producing a more discriminative encoding than several state-of-the art methods based only on intensity information. Then, spatial relationships among the neighboring pixels through the edge responses are exploited independently with the help of both LDP’s and LTP’s concepts to enhance the discrimination capability. Indeed, the implicit utilization of both concepts of LTP and LDP encodes more information in comparison to the existing directional and derivative methods in less space, and simultaneously allows discriminating more textures. Finally, the resultant LDTP pattern is divided into two distinct parts: local directional ternary pattern upper ( LDTP U ) and local directional ternary pattern lower ( LDTP L ), and the final feature descriptor vector is obtained by linear concatenation of both LDTP U and LDTP L histograms. The experiments carried out on nine publicly available texture datasets demonstrated that the proposed LDTP descriptor achieves classification performance, which is competitive or better than several recent and old state-of-the-art LBP variants. Statistical significance of the achieved accuracy improvement by the proposed descriptor has been also demonstrated through the Wilcoxon signed rank test applied on all the tested datasets. [ABSTRACT FROM AUTHOR]

In this paper, we propose a novel rotation invariant and noise tolerant descriptor for texture classification. This descriptor is based on circular statistics of Fourier phase. This one is recovered from the third order spectrum namely the bispectrum, known to provide invariance properties and to preserve phase information. The computational complexity of two dimensional image is reduced using Radon transform. At first, the input image is decomposed into a set of 1D radon projections. Then, for each projection the bispectrum is computed and the phase vector is recovered. Features vectors contain circular statistics of each phase vector recovered from bispectrum of each 1D projection. The proposed descriptor is evaluated on three test suites from the database “Outex” and compared with three descriptors also based on the phase. According to the classification experiments, our descriptor achieves highest rates under noise, illumination and rotation changes. [ABSTRACT FROM AUTHOR]

Building discriminative and robust texture representation to deal with the changes of texture appearance is a fundamental issue in texture classification. The Local Binary Pattern (LBP) and its variants gain a lot of attention during the past decade and achieve great success in texture description. However, the current existing LBP-based features which treat LBP as local differential or orientation gradient operator, exploited local orientation pattern or anisotropic structure information separately. In this paper, we investigate the theoretical scheme of local differential approximation on the polar coordinate system in order to build a new LBP-based descriptor which better takes into account both radial plus tangential components and derivative information. First, we present an operator called circumferential derivative (CD) based on the tangential information with different order of derivatives. Then, we present an operator called radial derivative (RD) based on the radial information with different order of derivatives. Both extract complementary information locally around a central pixel. A new descriptor, the local binary circumferential and radial derivative pattern (CRDP) is constructed to fuse both local circumferential and radial derivative features based on different orders as well as a global feature based on global difference (GD) of central pixel’s intensity. Extensive experiments on Outex, CUReT, KTH-TIPS and KTH-TIPS2-a texture datasets indicate that the proposed CRDP descriptor is discriminative and robust. The results obtained by the proposed CRDP descriptor outperforms more than twenty recent LBP-based state-of-the-art methods, including the best reported results in the literature for aforementioned texture datasets to the best of our knowledge. [ABSTRACT FROM AUTHOR]

Local binary patterns (LBP) and its variants have shown great potentials in texture classification tasks. LBP-like texture classification methods usually follow a two-step feature extraction process: in the first pattern encoding step, the local structure information around each pixel is encoded into a binary string; in the second histogram accumulation step, the binary strings are accumulated into a histogram as the feature vector of a texture image. The performances of these classification methods are closely related to the distinctiveness of the feature vectors. In this paper, we propose a novel feature representation method, namely Completed Discriminative Local Features (CDLF), for texture classification. The proposed CDLF improves the distinctiveness of LBP-like feature vectors in two aspects: in the pattern encoding stage, we learn a transformation matrix using labeled data, which significantly increases the discrimination power of the encoded binary strings; in the histogram accumulation step, we use an adaptive weight strategy to consider the contributions of pixels in different regions. The experimental results on three challenging texture databases demonstrate that the proposed CDLF achieves significantly better results than previous LBP-like feature representation methods for texture classification tasks. [ABSTRACT FROM AUTHOR]

In this paper, an efficient multiresolution method for texture classification based on anisotropic diffusion and local directional binary patterns (LDBP) is proposed. The method focuses on recognizing the most dominant LDBP descriptors that characterize the texture in an image by analyzing the effect of neighborhoods and radial distance of LDBP on texture classification. The texture descriptors are evaluated and compared on four texture datasets, namely, Brodatz, Oulu, VisTex and Kylberg. The discriminating power of multiresolution LDBP descriptors is assessed in classification experiments using k-NN classifier. The experimental results show that the proposed approach based on multiresolution LDBP descriptors and anisotropic diffusion yields better classification accuracy with low computational cost. The proposed method is then used in wood identification. The method is tested on fourteen texture images with varying color tones and number of samples per tone. The experimental results demonstrate the effectiveness of the proposed method in achieving the improved classification accuracy. [ABSTRACT FROM AUTHOR]

Deep learning has established many new state of the art solutions in the last decade in areas such as object, scene and speech recognition. In particular Convolutional Neural Network (CNN) is a category of deep learning which obtains excellent results in object detection and recognition tasks. Its architecture is indeed well suited to object analysis by learning and classifying complex (deep) features that represent parts of an object or the object itself. However, some of its features are very similar to texture analysis methods. CNN layers can be thought of as filter banks of complexity increasing with the depth. Filter banks are powerful tools to extract texture features and have been widely used in texture analysis. In this paper we develop a simple network architecture named Texture CNN (T-CNN) which explores this observation. It is built on the idea that the overall shape information extracted by the fully connected layers of a classic CNN is of minor importance in texture analysis. Therefore, we pool an energy measure from the last convolution layer which we connect to a fully connected layer. We show that our approach can improve the performance of a network while greatly reducing the memory usage and computation. [ABSTRACT FROM AUTHOR]

This paper examines 2D multifractal model based on operations taken from mathematical morphology. Firstly, there is given an extension of the existing multifractal model based on Legendre transform and dilation and complementary model using erosion is investigated. Two additional methods are proposed by introducing a new definition of local multifractal measures and two realizations of coverings of local dimensions images used for calculation of global dimensions. One of local measures is implemented using dilation and the other using erosion. Derived multifractal spectra are used as descriptors of textural images. Experiments conducted on referent textural datasets and in comparison to state of the art descriptors provide classification results of proposed descriptors that exceed performance of both multifractal and non-fractal descriptors from literature. [ABSTRACT FROM AUTHOR]

Recently, the Earth Mover׳s Distance (EMD) has demonstrated its superiority in Gaussian mixture models (GMMs) based texture classification. The ground distances between Gaussian components of GMMs have great influences on performance of GMM matching, which however, has not been fully studied yet. Meanwhile, image features play a key role in image classification task, and often greatly impact classification performance. In this paper, we present a comprehensive study of ground distances and image features in texture classification task. We divide existing ground distances into statistics based ones and Riemannian manifold based ones. We make a theoretical analysis of the differences and relationships among these ground distances. Inspired by Gaussian embedding distance and product of Lie Groups distance, we propose an improved Gaussian embedding distance to compare Gaussians. We also evaluate for the first time the image features for GMM matching, including the handcrafted features such as Gabor filter, Local Binary Pattern (LBP) descriptor, SIFT, covariance descriptor and high-level features extracted by deep convolution networks. The experiments are conducted on three texture databases, i.e., KTH-TIPS-2b, FMD and UIUC. Based on experimental results, we show that the uses of geometrical structure and balance strategy are critical to ground distances. The experimental results show that GMM with the proposed ground distance can achieve state-of-the-art performance when high-level features are exploited. [ABSTRACT FROM AUTHOR]

The histograms of oriented gradients (HOG) and co-occurrence HOG (CoHOG) algorithms are simple and intuitive descriptors. However, the HOG and CoHOG algorithms based on gradient computation still have some shortcomings: they ignore meaningful textural properties and are unstable to noise. In this paper, two new efficient HOG and CoHOG methods are proposed. The proposed algorithms are based on the Gaussian derivative filters, and the feature vectors are obtained by means of principal curvatures. The feature vectors are rotation invariant by means of the rotation invariance characteristic of principal curvatures (i.e. eigenvalues). The experimental results on the CUReT, KTH-TIPS, KTH-TIPS2-a, UIUC, Brodatz album, Kylberg and Xu datasets confirm that the developed algorithms have higher classification rates than state-of-the-art texture classification methods. The classification results also demonstrate that the developed algorithms are more stable to noise and rotation than the original HOG and CoHOG algorithms. [ABSTRACT FROM AUTHOR]

The aura concept has been developed from the set theory and is an efficient tool to characterize texture images. It is based on the notion of “aura set” and on the associated “aura measure” that involve the neighborhood of each image pixel. In this paper, we propose to extend this concept to the framework of fuzzy sets in order to take the imprecise nature of images into account. We define the notions of fuzzy aura sets and of aura measures to compute fuzzy aura matrices as texture descriptors. Fuzzy aura measures assume no restrictions about the neighborhood shape, size, and spatial invariance. Extensive tests of texture classification on Outex benchmark datasets show that fuzzy aura matrices computed with spatially variant neighborhoods often outperform other powerful texture descriptors on both gray-level and color images. [ABSTRACT FROM AUTHOR]

In this paper, we propose a simple and robust local descriptor operator, called the extended local graph structure (ELGS). The original local graph structure (LGS) performs very well in many domains, for instance face recognition, face spoofing detection and others. However, LGS has a few demerits such as LGS is not robust to the noise present in the image and LGS takes into considerations the horizontal graph and ignores the vertical graph which causes a loss in the spatial information. Therefore, we extend the idea of LGS by encoding the pattern into two directions. This is means that we take into consideration the vertical graph along with the horizontal graph and then concatenate the two computed histograms features to form a global descriptor. Experimental results on the UIUC and XU High Resolution texture databases show a promising performance. [ABSTRACT FROM AUTHOR]