Search

In agriculture, as precision farming increasingly employs robots to monitor crops, the use of weeding and harvesting robots is expanding the need for computer vision. Currently, most researchers and companies address these computer vision tasks with CNN-based deep learning. This technology requires large datasets of plant and weed images labeled by experts, as well as substantial computational resources. However, traditional feature-based approaches to computer vision can extract meaningful parameters and achieve comparably good classification results with only a tenth of the dataset size. This study presents these methods and seeks to determine the minimum number of training images required to achieve reliable classification. We tested the classification results with 5, 10, 20, 40, 80, and 160 images per weed type in a four-class classification system. We extracted shape features, distance transformation features, color histograms, and texture features. Each type of feature was tested individually and in various combinations to determine the best results. Using six types of classifiers, we achieved a 94.56% recall rate with 160 images per weed. Better results were obtained with more training images and a greater variety of features. [ABSTRACT FROM AUTHOR]

Globally, lung and colon cancers are among the most prevalent and lethal tumors. Early cancer identification is essential to increase the likelihood of survival. Histopathological images are considered an appropriate tool for diagnosing cancer, which is tedious and error-prone if done manually. Recently, machine learning methods based on feature engineering have gained prominence in automatic histopathological image classification. Furthermore, these methods are more interpretable than deep learning, which operates in a "black box" manner. In the medical profession, the interpretability of a technique is critical to gaining the trust of end users to adopt it. In view of the above, this work aims to create an accurate and interpretable machine-learning technique for the automated classification of lung and colon cancers from histopathology images. In the proposed approach, following the preprocessing steps, texture and color features are retrieved by utilizing the Haralick and Color histogram feature extraction algorithms, respectively. The obtained features are concatenated to form a single feature set. The three feature sets (texture, color, and combined features) are passed into the Light Gradient Boosting Machine (LightGBM) classifier for classification. And their performance is evaluated on the LC25000 dataset using hold-out and stratified 10-fold cross-validation (Stratified 10-FCV) techniques. With a test/hold-out set, the LightGBM with texture, color, and combined features classifies the lung and colon cancer images with 97.72%, 99.92%, and 100% accuracy respectively. In addition, a stratified 10-fold cross-validation method also revealed that LightGBM's combined or color features performed well, with an excellent mean auc_mu score and a low mean multi_logloss value. Thus, this proposed technique can help histologists detect and classify lung and colon histopathology images more efficiently, effectively, and economically, resulting in more productivity. [ABSTRACT FROM AUTHOR]

In the current trend, the image retrieval (IR) system has also been shifted from traditional text-based to content-based with the advancement in technology. Many issues have been resolved by content-based IR. In a particular case of information retrieval/image retrieval (IR) systems, the number of images on the web and the usage of images are growing exponentially. Therefore, IR system needs to be scalable, flexible, modularizable and promotes reusability so that it becomes easy to deploy, develop and maintain the system. In this paper, we have presented a CBIR model using Color Histogram and Local Binary Pattern (LBP) where both are built with Microservices architecture using docker platform. The framework used in this model is logic independent therefore any CBIR system can be run using this framework. The CBIR using Color Histogram uses chi-squared distance as a similarity measure while CBIR model using LBP is implemented using Linear Support Vector Machines for image classification. In our experiments, we have achieved the average recall, precision, and F-measure using Color Histogram 22.25%, 63.12%, and 32.67%, respectively. Though, we have achieved the average recall, precision, and F-measure using LBP 77.15%, 79.90%, and 76.17%, respectively. It has been observed that LBP model is more accurate than Color Histogram for detecting different weather conditions. It has also been found that the use of Microservices architecture leads to improve the non-functional qualities of a CBIR system as compared to traditional architecture styles by a great margin. [ABSTRACT FROM AUTHOR]

The advancement of digital microscopic scanning has made the study of image processing as well as categorization an exciting field of diagnostic studies. The literature describes a number of methods for detecting acute lymphocytic leukemia (ALL) using blood smear pictures. The goal of this research is to create an efficient approach for segmenting and detecting leukemia. This research has created a leukemia diagnosis module predicated on deep learning (DL) using blood smear pictures. Pre-processing, segmentation, extraction of features, and classification are performed here by the identification scheme. The presented hybrid model of African Buffalo and African Vulture Optimization (AB-AVO) performs the segmentation process, in which cytoplasm and nucleus regions are segmented. The Local Directional Pattern (LDP) and color histogram characteristics have been then retrieved from the segmented pictures and given into the presented Recurrent Neural Network (RNN) for categorization. The ALL-IDB1 and ALL-IDB2 databases' blood smear pictures are taken into account for the investigation and assessed using metrics including F1-score, sensitivity, dice coefficient, precision, specificity, recall, and accuracy. The presented AB-AVO-RNN approach exhibits 100% accuracy, according to simulation data. Modern methodologies are used to compare the effectiveness of the suggested AB-AVO-RNN methodology. The investigation demonstrates that the suggested classifier performs comparably better and can identify leukemia from blood smear pictures. [ABSTRACT FROM AUTHOR]

Multiple microbes can alter a plant's development and agricultural productivity, which has significant implications for the ecosystem and human life. As a result, timely identification, prevention, and prompt treatment are required. Fundamental methods have some drawbacks to plant disease identification like more time-consuming, accuracy, doesn't support multiple plant detection. This paper introduces a hybrid model that uses a random forest classifier combined with the AdaBoost Classifier to classify plant diseases to overcome the above-said drawbacks. So as to individualize normal and abnormal leaves from data sets, the suggested methodology employs the Random Forest with AdaBoost algorithm. The operational processes in our suggested study are preprocessing, segmentation, feature extraction, training the classifier, and classification. The produced datasets of infected and uninfected leaves are combined and processed using the Random Forest classifier to categorize the infected and uninfected photos. Color Histogram is used to gather features from imagery. KNN, Naive Bayes, and SVM are all used to evaluate our suggested technique. [ABSTRACT FROM AUTHOR]

In agriculture, as precision farming increasingly employs robots to monitor crops, the use of weeding and harvesting robots is expanding the need for computer vision. Currently, most researchers and companies address these computer vision tasks with CNN-based deep learning. This technology requires large datasets of plant and weed images labeled by experts, as well as substantial computational resources. However, traditional feature-based approaches to computer vision can extract meaningful parameters and achieve comparably good classification results with only a tenth of the dataset size. This study presents these methods and seeks to determine the minimum number of training images required to achieve reliable classification. We tested the classification results with 5, 10, 20, 40, 80, and 160 images per weed type in a four-class classification system. We extracted shape features, distance transformation features, color histograms, and texture features. Each type of feature was tested individually and in various combinations to determine the best results. Using six types of classifiers, we achieved a 94.56% recall rate with 160 images per weed. Better results were obtained with more training images and a greater variety of features.

The need for information is gradually shifting from text to images due to the technology’s growth and increase in digital images. It is quite challenging for people to find similar color images. To obtain similarity matching, the color of the image needs to be identified. This paper aims at various clustering techniques to identify the color of the digital image. Though many clustering techniques exist, this paper focuses on Fuzzy c-Means, Mean-Shift, and a hybrid technique that amalgamates the agglomerative hierarchies and k-Means, known as hKmeans to cluster the intensity of the image. Applying evaluation metrics of Mean Squared Error, Root Mean Squared Error, Mean Absolute Error, Homogeneity, Completeness, V-Score, and Peak signal-to-noise ratio it is proven that the results obtained demonstrate the good performance of the proposed technique. Then the color histogram is applied to identify the color and differentiate the color distribution on the original and clustered image.

Education informatization is an important means to improve the quality of education and teaching as well as a basic condition for the innovative reform and application of education and teaching, which also means that education informatization cannot be separated from the construction of digital learning resources. This paper utilizes the advantage of Flash technology’s integration of multiple media elements to design physics animation courseware. Then, it combines the color histogram and edge density analysis algorithm to segment the animated visual scene of physics courseware, optimize the production process of Flash physics animated courseware, and establish a scientific evaluation standard for courseware quality. The average scores of the four dimensions of manipulability, stability, aesthetics, and compatibility in the evaluation results of physics animated courseware in this paper are all higher than 4 points. The students who apply physics animated courseware to teach improve their learning attitudes, learning behaviors, and learning effects by 11.61, 7.33, and 6.54, respectively, compared with the traditional physics teaching, and all of them are significantly different (P

The digital world is developing more quickly than ever. Multimedia processing and distribution, however become vulnerable issues due to the enormous quantity and significance of vital information. Therefore, extensive technologies and algorithms are required for the safe transmission of messages, images, and video files. This paper proposes a secure framework by acute integration of video summarization and image encryption. Three parts comprise the proposed cryptosystem framework. The informative frames are first extracted using an efficient and lightweight technique that make use of the color histogram-clustering (RGB-HSV) approach's processing capabilities. Each frame of a video is represented by deep features, which are based on an enhanced pre-trained Inception-v3 network. After that summary is obtain using the K-means optimal clustering algorithm. The representative keyframes then extracted using the clusters highest possible entropy nodes. Experimental validation on two well-known standard datasets demonstrates the proposed methods superiority to numerous state-of-the-art approaches. Finally, the proposed framework performs an efficient image encryption and decryption algorithm by employing a general linear group function GLn (F). The analysis and testing outcomes prove the superiority of the proposed adaptive RSA.

Chickpea is one of the most important legumes in the world, however, it is prone to various diseases that can significantly reduce its yield and quality. Hence, the accurate classification of these diseases are crucial for effective disease management. In this study, we propose a combined approach for chickpea disease classification using GLCM-Color Histogram features with Bilateral filtering and non-local means filtering. Our research comprises three phases: image preprocessing, feature extraction, and classification. To enhance the model's robustness and reduce noise, we applied Bilateral filtering, non-local means filtering, and data augmentation techniques. We utilized a combination of gray-level co-occurrence matrix (GLCM) and Color Histogram for feature extraction, which can capture the texture and color features important for image classification tasks. The extracted features were then classified using Multi-Layer Perceptrons (MLPs), Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Random Forest (RF). The experimental results indicate that the combined features extracted using GLCM and Color Histogram with the SVM classifier outperformed individual feature extractors and classifiers, achieving a testing accuracy of 95.49 %. Our study demonstrates that proper image preprocessing, data augmentation, and feature extraction provide an efficient classification method for identifying and classifying chickpea disease.

Person re-identification (PRID) has been thoroughly researched in typical surveillance scenarios where various scenes are monitored by side-mounted, rectilinear-lens cameras. To date, few methods have been proposed for fisheye cameras mounted overhead and their performance is lacking. In order to close this performance gap, we propose a multi-feature framework for fisheye PRID where we combine deep-learning, color-based and location-based features by means of novel feature fusion. We evaluate the performance of our framework for various feature combinations on FRIDA, a public fisheye PRID dataset. The results demonstrate that our multi-feature approach outperforms recent appearance-based deep-learning methods by almost 18% points and location-based methods by almost 3% points in matching accuracy. We also demonstrate the potential application of the proposed PRID framework to people counting in large, crowded indoor spaces.

The video retrieval system refers to the task of retrieving the most relevant video collection, given a user query. By applying some feature extraction models the contents of the video can be extracted. With the exponential increase in video data in online and offline databases as well as a huge implementation of multiple applications in health, military, social media, and art, the Content-Based Video Retrieval (CBVR) system has emerged. The CBVR system takes the inner contents of the video frame and analyses features of each frame, through which similar videos are retrieved from the database. However, searching and retrieving the same clips from huge video collection is a hard job because of the presence of complex properties of visual data. Video clips have many frames and every frame has multiple properties that have many visual properties like color, shape, and texture. In this research, an efficient content-based video retrieval system using the AlexNet model of Convolutional Neural Network (CNN) on the keyframes system has been proposed. Firstly, select the keyframes from the video. Secondly, the color histogram is then calculated. Then the features of the color histogram are compared and analyzed for CBVR. The proposed system is based on the AlexNet model of CNN and color histogram, and extracted features from the frames are together to store in the feature vector. From MATLAB simulation results, the proposed method has been evaluated on benchmark dataset UCF101 which has 13320 videos from 101 action categories. The experiments of our system give a better performance as compared to the other state-of-the-art techniques. In contrast to the existing work, the proposed video retrieval system has shown a dramatic and outstanding performance by using accuracy and loss as performance evaluation parameters.

This article presents a new approach based on deep learning to automatically extract colormaps from visualizations. After summarizing colors in an input visualization image as a Lab color histogram, we pass the histogram to a pre-trained deep neural network, which learns to predict the colormap that produces the visualization. To train the network, we create a new dataset of $\sim$ ∼ 64K visualizations that cover a wide variety of data distributions, chart types, and colormaps. The network adopts an atrous spatial pyramid pooling module to capture color features at multiple scales in the input color histograms. We then classify the predicted colormap as discrete or continuous, and refine the predicted colormap based on its color histogram. Quantitative comparisons to existing methods show the superior performance of our approach on both synthetic and real-world visualizations. We further demonstrate the utility of our method with two use cases, i.e., color transfer and color remapping. [ABSTRACT FROM AUTHOR]

Due to the posteriori knowledge provided by the Human-Computer Interaction algorithms, interactive segmentation based on Graph Cuts can successfully extract the foreground in an image, which will, however, limit the scope of their application. Inspired by recent years of research on computer vision with fully convolutional neural networks, a fully automated image segmentation method based on FCN and Graph Cuts has been proposed. First, FCN is employed to obtain an original mask for the input image, but its boundary is poor. Second, we generate seed regions heuristically using color histograms and mathematical morphological operations on the original mask. Finally, iterative segmentation is performed using generative seeds and superpixel-level Graph Cuts. Experimental results show that our method has higher segmentation accuracy compared to other representative methods including interactive and automatic segmentation. [ABSTRACT FROM AUTHOR]

Content based image retrieval (CBIR) systems enable a quick retrieval of similar images from a large digital repository. However, the performance of the system is heavily reliant on the feature definition of images. The challenge lies in extracting suitable features that can work across a variety of datasets. In this paper, Haar-like local ternary co-occurrence pattern (HLTCoP) is designed as a feature for image retrieval applications. In HLTCoP, four different Haar-like filters are deployed to capture directional information of the image and two different local neighborhoods are considered to obtain the local patterns around every pixel. Thereafter, co-occurrence between two filtered images is computed to construct the HLTCoP feature. Additionally, color information is extracted using histograms of hue and saturation planes. Image retrieval performance is verified on diversified benchmark datasets, Corel 10k, CMU-PIE and MIT VisTex. Significant improvement is achieved in comparison to the existing techniques. [ABSTRACT FROM AUTHOR]

Emotions can be evoked in humans by images. Previous reports on Recognition of Emotions induced by Visual Content of images (REVC) mainly focused on numerous features to improve recognition performance. To devise a more robust REVC system, this paper examines the performance of a wide range of classifiers using color histogram as a single feature. Different numbers of color histogram bins in both RGB (red, green, blue) and HSV (hue, saturation, value) color spaces are considered in the examination and the overall classification performance is compared across the bin sizes. This investigation shows that features are not the only important factors affecting the performance of REVC systems, but also the type of classifiers and their parameters. This study shows that the HSV color space is better suited than the RGB color space for REVC systems. This paper proposes a new optimization algorithm called Optimizing Parameters of Ensemble RUSboosted Tree (OPERT) to boost the performance of the REVC system. Furthermore, a novel REVC system called Color histogram with Optimized RUSboosted Tree (CORT) is introduced. It is shown that our method is simpler, faster, and more efficient than the state-of-the-art, while providing comparable recognition performance. The robustness of the CORT system is validated over three different image datasets.

Correlation filter-based trackers have gained more and more attention because of their great performances and relative high tracking speeds. However, this kind of trackers may suffer model drifting due to learning limited background information during filter training. This may lead to tracking failures in some complex scenes, such as background clutter, deformation, illumination variation and so on. In this article, we propose an adaptive and complementary correlation filter with dynamic contextual constraints. First, we introduce contextual information around the target as a dynamic constrained term to alleviate model drifting in complex scenes, the optimal function of which can be solved by an iterative method. Then, we integrate a color histogram-based tracker to compensate the inaccurate tracking of correlation filtering. In addition, we present metrics to combine the two complementary trackers with adaptive fusion coefficients. Finally, extensive experiments on OTB2013, OTB2015, VOT2016 and UAV123 benchmark datasets demonstrate that our tracker can improve the performance of our baseline and can perform favorably against some state-of-the-art trackers.

In the paper, we propose color content descriptors of images by vector quantization in RGB color space in order to obtain a much more accurate discrete representation of this space using our algorithm for optimization of vector quantizers compared to scalar ones. We introduce several modifications of these descriptors such as global, structural as well as of dominant color. We consider a different number of bins and evaluated the similarity of the color content of images using mean square error of color histograms of a reference image and a searched image. Then, the color content of the image with the minimum error was the most similar to the reference image. We also used a parameter of variance, if the color content of several searched images was very similar based on the mean quadratic error for the structural descriptor.

Abstract Wireless capsule endoscope allows painless endoscopic imaging of the gastrointestinal track of humans. However, the whole procedure will generate a large number of capsule endoscopy images (CEIs) for reading and recognizing. In order to save the time and energy of physicians, computer-aided analysis methods are imperatively needed. Due to the influence of air bubble, illumination, and shooting angle, however, it is difficult to classify CEIs into healthy and diseased categories correctly for a conventional classification method. To this end, in the paper, a new feature extraction method is proposed based on color histogram, wavelet transform, and co-occurrence matrix. First, an improved color histogram is calculated in the HSV (hue, saturation, value) space. Meanwhile, by using the wavelet transform, the low-frequency parts of the CEIs are filtered out, and then, the characteristic values of the reconstructed CEIs’ co-occurrence matrix are calculated. Next, by employing the proposed feature extraction method and the BPNN (back propagation neural network), a novel computer-aided classification algorithm is developed, where the feature values of color histogram and co-occurrence matrix are normalized as the inputs of the BPNN for training and classification. Experimental results show that the accuracy of the proposed algorithm is up to 99.12% which is much better than the compared conventional methods.

Numerous object-tracking and multiple-person-tracking algorithms have been developed in the field of computer vision, but few trackers can properly address the issue of when a pedestrian is partially or fully occluded by other objects or persons. In order to achieve efficient pedestrian tracking in various occlusion conditions, a pedestrian tracking framework is proposed and developed based on the deep learning networks. First, a pedestrian detector is trained as a tracking mechanism based on the Faster R-CNN, which narrows the search range and efficiently improves accuracy, as compared with the traditional gradient descent algorithm. Second, in the process of target matching, a color histogram and scale-invariant feature transform are combined to provide the target model expression, and a full convolution network (FCN) is trained to extract the pedestrian information in the target model, based on an FCN image semantic segmentation algorithm that can remove background noise effectively. Finally, the extensive experiments on a commonly used tracking benchmark show that the proposed method achieves better performance than the other state-of-the-art trackers in various occlusion situations.

Content based image retrieval (CBIR) systems provide a faster way to retrieve images by representing them in terms of their visual contents. In this paper, a novel texture feature, directional local ternary co-occurrence pattern (DLTCoP) is proposed for CBIR. First and second order derivatives of the image are extracted through directional filter masks to capture coarse and fine details of the image in four directions. Thereafter, changes in first and second order filter responses are analyzed simultaneously and co-occurrence is computed based on their inter-relations. The information captured by DLTCoP is further enriched by computing histograms for the gray-scale image and the color information is represented as color histograms. The proposed scheme provides a consolidated feature capable of distinguishing between different images. Experiments are conducted on five benchmark data sets, Corel 1000, Corel 5k, Corel 10k, INRIA Holidays and Salsburg Texture. Significant improvement in average precision and recall is obtained with respect to the existing state-of-the-art features. [ABSTRACT FROM AUTHOR]

In the paper, we propose color content descriptors of images by vector quantization in RGB color space compared to scalar ones. In order to obtain a much more accurate discrete representation of this space we use our algorithm for optimization of vector quantizers. We introduce several modifications of these descriptors such as global, structural as well as of dominant color. We consider a different number of bins and evaluated the similarity of the color content of images using mean square error of color histograms of a reference image and a searched image. Then, the color content of the image with the minimum error was the most similar to the reference image. We also used a parameter of variance, if the color content of several searched images was very similar based on the mean quadratic error for the structural descriptor. By vector quantization of RGB space we can achieve 2-3 times decreasing the number of bins at the same accuracy. [ABSTRACT FROM AUTHOR]

Wireless capsule endoscopy (WCE) is the most advanced technology to visualize whole gastrointestinal (GI) tract in a non-invasive way. But the major disadvantage here, it takes long reviewing time, which is very laborious as continuous manual intervention is necessary. In order to reduce the burden of the clinician, in this paper, an automatic bleeding detection method for WCE video is proposed based on the color histogram of block statistics, namely CHOBS. A single pixel in WCE image may be distorted due to the capsule motion in the GI tract. Instead of considering individual pixel values, a block surrounding to that individual pixel is chosen for extracting local statistical features. By combining local block features of three different color planes of RGB color space, an index value is defined. A color histogram, which is extracted from those index values, provides distinguishable color texture feature. A feature reduction technique utilizing color histogram pattern and principal component analysis is proposed, which can drastically reduce the feature dimension. For bleeding zone detection, blocks are classified using extracted local features that do not incorporate any computational burden for feature extraction. From extensive experimentation on several WCE videos and 2300 images, which are collected from a publicly available database, a very satisfactory bleeding frame and zone detection performance is achieved in comparison to that obtained by some of the existing methods. In the case of bleeding frame detection, the accuracy, sensitivity, and specificity obtained from proposed method are 97.85%, 99.47%, and 99.15%, respectively, and in the case of bleeding zone detection, 95.75% of precision is achieved. The proposed method offers not only low feature dimension but also highly satisfactory bleeding detection performance, which even can effectively detect bleeding frame and zone in a continuous WCE video data.

Existing Discriminative Correlation Filter (DCF) based methods suffer from the limitations of rectangular shape assumptions. Aiming at this issue, in this paper, we propose an effective tracking approach which integrates a pixel-wise foreground segmentation mask into the correlation filter within a multi-feature fusion framework. Specifically, we first propose a novel segmentation algorithm which combines the color histogram with the spatial prior. On this basis, we implement a target-masked correlation filter (TMCF) tracker by introducing the foreground mask into a ridge regression, which successfully suppresses unexpected background information inside the bounding box. Secondly, we apply the alternating direction method of multipliers (ADMM) to solve our TMCF model efficiently to obtain the closed-form solution. Finally, a complementary fusion tracker by the combining of TMCF and color histogram scores (fTMCFCH) is formulated, which is robust to deformations and illumination changes simultaneously. The fusion factor is determined adaptively by the reliability derived from the target resolution of the trackers separately in each frame. We perform extensive experiments on three benchmarks: OTB-2013, OTB-2015 and Temple-Color-128. The concrete experimental results demonstrate that our tracker outperforms several state-of-the-art trackers. [ABSTRACT FROM AUTHOR]

Automated recognition and classification of fishes are useful for studies dealing with counting of fishes for population assessments, discovering association between fishes and ecosystem, and monitoring of the ecosystem. This paper proposes a model which classifies the fishes belonging to the family Labridae in the genus and the species level. Features computed in the spatial and frequency domains are used in this work. All the images are preprocessed before feature extraction. Preprocessing step involves image segmentation for background elimination, de-noising and image enhancement. A combination of color, local binary pattern (LBP), histogram of oriented gradients (HOG), and wavelet features forms the feature vector. An ensemble feature reduction technique is used to reduce the attribute size. Performances of the system using combined as well as reduced feature sets are evaluated using seven popular classifiers. Among the classifiers, wavelet kernel extreme learning machine (ELM) showed higher classification accuracy of 96.65% in genus level and polynomial kernel ELM showed an accuracy of 92.42% in species level with the reduced feature set. [ABSTRACT FROM AUTHOR]

A common drawback of convolutional features based trackers is the incapability of distinguishing tracking targets from distractors, even in the presence of distinct color difference. In this paper, we design a robust hybrid tracker that explicitly combines color features with convolutional features. We design our tracker on the basis of fully convolutional Siamese network (SiamFC) to emphasize the performance promotion by introducing color features instead of using a more advanced network architecture. A novel approach to integrate two score maps from different channels is proposed. Techniques include cropping out the ineffective area and denoising via Gaussian smoothing. Experiments conducted on OTB2015 and VOT2018 benchmarks show the superiority of our hybrid tracker over the original SiamFC. [ABSTRACT FROM AUTHOR]

We propose a robust visual object tracking algorithm fusing a convolutional neural network tracker trained offline from a large number of video repositories and a color histogram based tracker to track objects for mixing immersive audio. Our algorithm addresses the problem of occlusion and large movements of the CNN based GOTURN generic object tracker. The key idea is the offline training of a binary classifier with the color histogram similarity values estimated via both trackers used in this method to opt appropriate tracker for target tracking and update both trackers with the predicted bounding box position of the target to continue tracking. Furthermore, a histogram similarity constraint is applied before updating the trackers to maximize the tracking accuracy. Finally, we compute the depth(z) of the target object by one of the prominent unsupervised monocular depth estimation algorithms to ensure the necessary 3D position of the tracked object to mix the immersive audio into that object. Our proposed algorithm demonstrates about 2% improved accuracy over the outperforming GOTURN algorithm in the existing VOT2014 tracking benchmark. Additionally, our tracker also works well to track multiple objects utilizing the concept of single object tracker but no demonstrations on any MOT benchmark. [ABSTRACT FROM AUTHOR]

This paper proposes a color image segmentation method based on region salient color and the fuzzy C-means (FCM) algorithm. The method first uses the convex hull theory based on Harris corner detection to detect the object of the image. Thus, the object and the background can be separated. Then, the quantized color histogram can be studied in the HSV color space. By calculating the number of the peak values of both the object and the background histograms, the quantity of the regional salient colors can be obtained. The quantity is the number of the clustering centroids of FCM algorithm. Finally, the FCM algorithm and the noise correction algorithm can be used in the object and the background, respectively. The obtained segmented image consists of the object and the background segmentation. It proves that the method in this paper is an effective segmentation method based on the experiments made by use of Berkeley segmentation dataset. According to the experimental results, it can be concluded that the proposed algorithm has the highest segmentation accuracy and the shortest computing time among the algorithms mentioned in this paper. The algorithm can achieve high-quality, stable and accurate color image segmentation results. [ABSTRACT FROM AUTHOR]

This paper presents a novel method that absorbs the strong discriminative ability from absent color indexing (ABC) to enhance sensitivity and combines it with a correlation filter (CF) for obtaining a higher precision; this method is named ABC-CF. First, by separating the original color histogram, apparent and absent colors are introduced. Subsequently, an automatic threshold acquisition is proposed using a mean color histogram. Next, a histogram intersection is selected to calculate the similarity. Finally, CF follows them to solve the drift caused by ABC during the matching process. The novel approach proposed in this paper realizes robustness in distortion of target images and higher margins in fundamental matching problems, and then achieves more precise matching in positions. The effectiveness of the proposed approach can be evaluated in the comparative experiments with other representative methods by use of the open data.

Co-saliency detection, which aims to detect common salient objects in a group of images, has attracted much attention in the field of computer vision. In this paper, we present an effective co-saliency detection approach that first exploits an efficient manifold ranking scheme to extract a set of co-saliency regions, and then renders rank constraint to the feature matrix of the extracted regions to achieve a high-quality co-saliency map. Specifically, for each input image, we first develop a two-stage manifold ranking algorithm to generate multiple coarse co-saliency maps, and then we extract a group of co-salient regions from each image by fusing the co-saliency maps and the superpixels extracted from it. Then, we design an adaptive weight for each co-saliency map based on the sparse error matrix that is obtained by rendering rank constraint on the feature matrix of the salient regions. Finally, we multiply the coarse co-saliency maps with their corresponding weights to get the fine fusion results, which are further optimized by Graph cuts. Extensive evaluations on the iCoseg dataset demonstrate favorable performance of the proposed approach over some state-of-art methods in terms of both qualitative and quantitative results. [ABSTRACT FROM AUTHOR]

In this paper, we propose biogeography particle swarm optimization (BPSO) approach for content based image retrieval using low-level features like color and texture. This approach is based on the content used in the image and its classification. The above-mentioned BPSO is inspired by migrated species biogeographically. The proposed approach is based on algorithms like color, histogram and texture which are applicable on color images. The use of these algorithms ensures that the suggested image retrieval approach fabricates such results which are highly applicable to the content of an image query. Features such as distance measure, color and histogram are used to find out color features of an image and filters are used to extract the texture feature. Well know precision and recall measures are used and the output results are compared with other recently used related approaches. Our proposed approach is having better performance as compared to past approaches as it uses features based on color and texture. [ABSTRACT FROM AUTHOR]

The perfect image retrieval and retrieval time are the two major challenges in CBIR systems. To improve the retrieval accuracy, the whole database is searched based on many image characteristics such as color, shape, texture and edge information which leads to more time consumption. This paper presents a new fuzzy based CBIR method, which utilizes colour, shape and texture attributes of the image. Fuzzy rule based system is developed by combining color, shape, and texture feature for enhanced image recovery. In this approach, DWT is used to pull out the texture characteristics and the region based moment invariant is utilized to pull out the shape features of an image. Color similarity and texture attributes are extorted using customized Color Difference Histogram (CDH). The performance evaluation based on precision and BEP measures reveals the superiority of the proposed method over renowned obtainable approaches. [ABSTRACT FROM AUTHOR]

Copyright of Diyala Journal for Pure Science is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Object tracking has been one of the most important and active research areas in the field of computer vision. In order to solve low accuracy in object occlusion and deformation for multi-object tracking, an online learned Hough forest model based on improved multi-feature fusion matching for multi-object tracking is proposed in this paper. Firstly, positive and negative samples are selected online according to low-level association among detection responses and construct the feature model of the object with color histogram, histogram of oriented gradient (HOG) and optical flow information. Secondly, longer trajectory associations are generated based on the online learned Hough forest framework. Finally, a trajectory matching algorithm based on multi-feature fusion is proposed, and we introduce two methods of similarity measure in color histogram and feature points matching based on the Gabor filter to generate the probability matrix with the weighted factor. Therefore, it can further form the complete trajectories of the objects by associating them gradually. We evaluate our approach on three public data sets, and show significant improvements compared with state-of-art methods. [ABSTRACT FROM AUTHOR]

Object tracking has been one of the most important and active research areas in the field of computer vision. In this paper, we address the problem of object tracking under complex conditions in a video, which propose a object tracking method based on particle filter of adaptive patches combined color histograms with Histogram of Oriented Gradient(HOG). The adaptive patch is performed by horizontal and vertical projection based on object gray levels, which can improve the patch adaptability to the object appearance diversity and the accuracy of object tracking under occlusion conditions. The fusion of color histograms and HOG features is adopted to describe each sub-patch, which not only solves the tracking divergence problem of similar objects, but also reduces the effect of local deformation. In addition, the weighted Bhattacharyya coefficient is introduced to calculate the sub-patch matching degree of the particle, and the particle sub-patch weight will be adjusted by integrating the particle space information, and the feature model is also updated in time to achieve robust object tracking. Many simulation experiments show that our proposed algorithm achieves more favorable performance than these existing state-of-the-art algorithms in handing various challenging videos, especially occlusion and shape deformation. [ABSTRACT FROM AUTHOR]

We propose a method for estimating the weight of the light source position to content-based image retrieval (CBIR) through retrieving image data. The image retrieval method makes use of a multi-dimensional descriptor expressing the features of the image. A multi-directional Gabor filter is then applied in order to extract the contour direction of the image, and a color correlation is applied in order to analyze the distribution of the position between the colors in the image. An HSV histogram and chromatic distribution information were obtained for the color composition analysis, and the light source position in the image was estimated for the main region extraction. The relative similarity between the images was measured through a relative comparison between each descriptor. In conventional image retrieval, there is a disadvantage in terms of the large computational intensity required, because the accuracy is degraded when simple information is used or when the result is estimated through a comparison of complex image components. However, the proposed method adds a light source location analysis element in order to ensure accurate retrieval. Image retrieval using the proposed method achieves an average retrieval accuracy of 85%, indicating that its reliability is superior to that of previously described methods. [ABSTRACT FROM AUTHOR]

This study investigates the use of the continuously adaptive mean shift (CamShift) algorithm in high-speed vision extraction. Videos of a high-speed target are captured with a high-speed camera. The displacement of the target is extracted from the videos using the CamShift algorithm. The CamShift algorithm is then compared with the normalized cross correlation algorithm and other algorithms in terms of accuracy and rapidity. Simulation and test results indicate that the CamShift algorithm is better than other algorithms in terms of the displacement extraction of high-frequency vibration. The CamShift algorithm also offers numerous advantages, including real time, high efficiency, high accuracy, and robustness. [ABSTRACT FROM AUTHOR]

Content based image retrieval (CBIR) techniques have been widely deployed in many applications for seeking the abundant information existed in images. Due to large amounts of storage and computational requirements of CBIR, outsourcing image search work to the cloud provider becomes a very attractive option for many owners with small devices. However, owing to the private content contained in images, directly outsourcing retrieval work to the cloud provider apparently bring about privacy problem, so the images should be protected carefully before outsourcing. This paper presents a secure retrieval scheme for the encrypted images in the YUV color space. With this scheme, the discrete cosine transform (DCT) is performed on the Y component. The resulting DC coefficients are encrypted with stream cipher technology and the resulting AC coefficients as well as other two color components are encrypted with value permutation and position scrambling. Then the image owner transmits the encrypted images to the cloud server. When receiving a query trapdoor form on query user, the server extracts AC-coefficients histogram from the encrypted Y component and extracts two color histograms from the other two color components. The similarity between query trapdoor and database image is measured by calculating the Manhattan distance of their respective histograms. Finally, the encrypted images closest to the query image are returned to the query user. [ABSTRACT FROM AUTHOR]

Inn then last two decades the Content Based Image Retrieval (CBIR) considered as one of the topic of interest for the researchers. It depending one analysis of the image's visual content which can be done by extracting the color, texture and shape features. Therefore, feature extraction is one of the important steps in CBIR system for representing the image completely. Color feature is the most widely used and more reliable feature among the image visual features. This paper reviews different methods, namely Local Color Histogram, Color Correlogram, Row sum and Column sum and Colors Coherences Vectors were used to extract colors features taking in consideration the spatial information of the image. [ABSTRACT FROM AUTHOR]

Perceptual image hashing technique uses the appearance of the digital media object as human eye and generates a fixed size hash value. This hash value works as digital signature for the media object and it is robust against various digital manipulation done on the media object. This technique have been constantly in use in various application areas like content-based image retrieval, image authentication, digital watermarking, image copy detection, tamper detection, image indexing, etc., but it is difficult to generate a perfect perceptual image hash function due to the inverse relationship between its main properties i.e. perceptual robustness and discriminative capability. In this paper, a robust and desirable discrimination capable dual perceptual image hash functions are proposed which use fuzzy color histogram for hash generation. The fuzzy engine needs stable color representation to generate a robust fuzzy color histogram feature which is invariant to various content preserving attacks like gaussian low pass filtering, jpeg compression, etc. To satisfy this, CIEL∗a∗b∗ color space forms an good basis as it approximates the human visual system and it is also uniform and device independent color space. The robustness of the fuzzy color histogram is further increased by selecting the most significant bins using an experimentally selected tuning factor and the same is furthermore normalized to make it scale invariant. Our experimentation shows that hash generated with this feature is more stable and able to handle various content preserving attacks and performs better as compared to the latest techniques. Both the proposed systems able to maintain good balance between perceptual robustness with optimal TPR when the FPR ≃0 is 0.8115 and 0.8264 and discrimination capability with the optimal FPR when TPR≃1 is 0.0618 and 0.0208 respectively. [ABSTRACT FROM AUTHOR]

This paper presents an automated multiple color making system. The various stages of work operations included in the adaptation of manually operated color mixing systems towards fully automated multiple colors making system. The proposed system can extract color features of an image and returns the intensities of Red, Green and Blue colors for a specified pixel in the given image. Since RGB Color Model is used for digital communications like televisions and computer monitors, and CMYK Color Model is used foprint ing and painting. So, for mixing purpose; the intensities of RGB are then converted into the intensities of Cyan, Magenta, Yellow and Black colors which then further converted into the form of electrical signals. Generally, from three primary colors CMY, a wide range of colors will be made with different proportions. The electrical signals will then be transmitted serially to our Logic Controller for mixing process, that sense the level of primary colors to be mixed and then mix colors proportionally in a container accordingly up to a fixed level in order to make different multiple colors of fixed quantity. Simulation results shows that the proposed methodology removes the chances like variation in quantity or inaccuracy which happened with the manually color sensing and mixing. It reduces the labor efforts and makes 7% more accurate with minimum error and achieve good retrieval performance. [ABSTRACT FROM AUTHOR]

The facial morphologies of identical twins were compared using the 3dMD three-dimensional (3D) photogrammetry system. 3D images of the faces of 27-year-old identical twins were acquired and then superimposed. The differences were shown in a color histogram generated using 3dMD Vultus software. The faces of the twins differed in the areas of the zygomatic region, lips, and cheeks. There were no prominent differences in other parts of their faces. Differences in the facial morphology of twins can be detected by superimposing 3D photogrammetry generated images and displaying the data as a color histogram. [Med-Science 2016; 5(2.000): 637-44]

Despite many years of research, object tracking remains a challenging problem, not only because of the variety of object appearances, but also because of the complexity of surrounding environments. In this research, we present an algorithm for single object tracking using a particle filter framework and color histograms. Particle filters are iterative algorithms that perform predictions in each iteration using particles, which are samples drawn from a statistical distribution. Color histograms are embedded in these particles, and the distances between histograms are used to measure likelihood between targets and observations. One downside of color histograms is that they ignore spatial information, which may produce tracking failure when objects appear that are similar in color. To overcome this disadvantage, we propose a saliency-based weighting scheme for histogram calculation. Given an image region, first its saliency map is generated. Next, its histogram is calculated based on the generated saliency map. Pixels located in salient regions have higher weights than those in others, which helps preserve the spatial information. Experimental results showed the efficiency of the proposed appearance model in object tracking under various conditions. [ABSTRACT FROM AUTHOR]

Based on Bag of Visual Words (BoVWs) model, this paper proposes a novel method using an integrated feature to detect sign text in the street view images. BRISK features are first extracted from the street view images for dictionary learning. The Self-Growing and Self-Organized Neural Gas (SGONG) network is then used to cluster adaptively the extracted BRISK descriptors for generating visual words. The histogram of visual words is further calculated to form the appearance feature of the sign text. For eliminating the color differences and further highlighting the histogram similarity of all colors of signs, a color invariant histogram, called CIHS histogram, is presented to represent the color information of the sign text. By integrating the visual words histograms and CIHS histograms, an integrated descriptor, called Appearance and Color (A&C) descriptor, is specifically designed as the input features for cascade-Adaboost classifier. In the multi-scale sliding window text sign detection, integral image is applied to the spatial distribution map of each visual word for avoiding repeated extraction of features. Experimental results demonstrate that the proposed method outperforms the state-of-the-art methods and the detectors with the traditional descriptors. [ABSTRACT FROM AUTHOR]

Color vector angle (CVA) is an important feature of processing color images and has been successfully developed and used in real applications, such as edge detection, indexing and retrieval of images. However, it is unsolved how to apply the CVA to efficiently generating an image hash. Also, most image hashing algorithms choose luminance component of color image for hash generation and cannot well capture the color information of images. To tackle these issues, we study efficient image hashing algorithms with the histogram of CVAs, called HCVA hashing. The histogram is first extracted from those angles that are in the biggest circle inscribed inside the normalized image. And then, it is compressed to make a short hash. We conducted some experiments to assess the performance, and illustrated that the DCT (Discrete Cosine Transform) is the best one of that cooperating with HCVA at generating hashes, as well as the HCVA hashing is robust and promising. [ABSTRACT FROM AUTHOR]

In order to develop an efficient angiosperm-genus classification system, we first collected petal image of Hibiscus, Orchis, and Prunus, by digital camera, and remove the backgrounds by region-growing method. Next, we proposed a novel feature-extraction method, which combined most abundant color index (MACI) and introduced the fractional Fourier entropy (FRFE). Third, we submitted the 41 features to a single-hidden layer feedforward neural-network (SLFN), with weight decay (WD) to avoid overfitting. The 10 × 10-fold cross validation showed our method achieved an overall accuracy of 98.92%. Without weight decay, the overall accuracy decreased to 95.50%. Our experiments validated that optimal decay factor is 0.1, and optimal number of hidden neurons is 15. This proposed method is excellent. It performs better than six state-of-the-art approaches and AlexNet. The weight decay helps to enhance generalization of our classifier. [ABSTRACT FROM AUTHOR]

In this paper, we propose a content-based image retrieval scheme in discrete cosine transform compressed domain with the help of genetic algorithm (GA). A combination of three image features, i.e., color histogram, color moments, and edge histogram, is extracted directly from the compressed domain and is used for similarity matching using the Euclidian distance. However, all the above image features are not equally important in image retrieval. Before similarity matching, GA is used to provide optimal weight factor (importance) on the image features to improve the system performance. Extensive experiments are carried out on three publicly available databases, and the comparison results demonstrate the outperforming performance of the proposed method over state-of-the-art techniques. Moreover, it is also seen that the use of GA improves 5.83% in precession (P), 6.36% in recall (R), and 5.84% in F-score values over non-GA-based techniques. [ABSTRACT FROM AUTHOR]

The purpose of this work is twofold: A fusion framework is proposed wherein the color histogram (CH), orthogonal combination of local binary patterns (OC-LBP), and color difference histogram (CDH) features are exploited to capture color, texture and shape information of an image, and a detailed comparative analysis of classical distance measures with non-linear support vector machine classifier (SVM) is presented. The proposed fusion is compared with individual and other fused features such as CH, OC-LBP, CDH, OC-LBP + CH, CH + CDH, OC-LBP + CDH in the L*a*b* color space. Detailed experiments reveal that the non-linear SVM classifier with pre-computed square-chord kernel, when used with any feature, outperforms other kernels and classical measures in terms of recognition rate on five datasets: SIMPLIcity / Wang , OT-Scene , Corel-5K , Corel-10K , and UKbench . Further, the proposed fused features i.e. CH + OC-LBP + CDH using non-linear SVM classifier with pre-computed square-chord kernel gives the best accuracy for all the aforementioned datasets. [ABSTRACT FROM AUTHOR]

To avoid misclassification during retrieval,this paper proposes an efficient multi-layer multi-level color distribution (MLMLCD) approach to improve the image retrieval quality. Here, the MLMLCD Vector Generation stage applies the wavelet transform over the image layers and hence color distribution vectors are generated. In MLMLCD Image Retrieval stage, the similarity measurement of MLMLCD color distribution vector value is made between the proposed technique and the values from larger databases. Finally, the precise retrieval result is produced with user feedback and query model, which is iterated over several runs. The performance of the proposed technique is tested between two datasets namely: McGill and CalTech database. Here, the performance is tested in terms of retrieval efficiency, classification rate and time complexity. A higher retrieval efficiency (98.5%)with less false classification rate(3.4%) is achieved, when compared with conventional techniques and a significance improvement is noted. [ABSTRACT FROM AUTHOR]