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Tuna is superior among the marine fishes that are exported in the forms of raw fish and processed food. Separation of Tuna into their species is done in industries manually, and the process is tiresome. This work proposes an automated system for classifying Tuna species based on their images. An ensemble of region-based deep neural networks is used. A sub region contrast stretching operation is applied to enhance the images. Each fish image is then divided into three regions and is augmented before giving as input to pre-trained convolutional neural networks (CNN). After fine-tuning the models, the output from the last convolutional layer is given to a grouped 2D-local binary pattern descriptor (G2DLBP). Statistical features from the descriptor are applied to different classifiers, and the best classifier for each image region model is identified. Different ensemble methods are subsequently used to combine the three CNN-G2DLBP models. Among the ensemble techniques, super learner ensemble method with random forest (RF) classifier using 5-fold cross-validation shows the highest classification accuracy of 97.32%. The performance of different ensemble methods is analyzed in terms of accuracy, precision, recall, and f-score. The proposed system shows an accuracy of 93.91% when evaluated with an independent test dataset. An ensemble of region-based CNN with textural features from G2DLBP is applied for the first time for fish classification.

This paper presents a new facial feature descriptor called Fused Cross Lattice Pattern of Phase Congruency (FCLPPC) for high accuracy homogeneous and heterogeneous illumination invariant intra/inter-modality face recognition. Using the dimensionless phase congruency features, an effective homogeneous and heterogeneous illumination invariant local feature extractor has been devised. Finally, a novel multi-directional binary pattern named Cross Lattice Pattern (CLP) has been proposed. CLP is applied to the previously extracted invariant phase congruency feature map to generate the CLPPC images. Weighted alpha-blending has been performed on the CLPPC maps to generate Fused CLPPC (FCLPPC) map. Recognition results on Extended Yale-B, TUFTS, CMU-PIE, and CASIA NIR-VIS dataset has been presented to depict the superiority of the proposed scheme over other state-of-the-art methods. Additionally, the proposed FCLPPC has been combined with a lightweight Convolutional Neural Network to further augment the recognition accuracy.

Nowadays, fault classification of the electric motors has become a hot-topic research area. Therefore, many machine learning method has been presented to create an intelligent fault detection system for electric motor. In this work, a novel classification method is presented for five different fault conditions of a motor and its drive system by using dynamic centered one-dimensional local angular binary pattern (DC-1D-LABP). It is proposed a novel multi-leveled feature extraction network, and one-dimensional discrete wavelet transform (1D-DWT) is used in order to create levels. Features are extracted from each level by using the proposed DC-1D-LABP. Neighborhood component analysis (NCA) and ReliefF-based 2-layered feature selector (NCARF) are used to select most discriminative features, and four conventional classifiers are selected for testing. A novel fault dataset is acquired, and this dataset is used for tests. Four cases were defined according to input current signal. The achieved best classification accuracy rates are 0.9692, 0.9571, 0.9650 and 1 for Case 1, Case 2, Case 3 and Case 4, respectively. These results indicate that the proposed DC-1D-LABP-based method is very effective for fault classification. Consequently, it is proposed a highly accurate and cognitive method for a fault classification in this study.

In recent years, the number of maize disease species has increased, which obviously increases the level of damages in leaves. The reason for maize leaf disease is due to variations in agriculture systems, the variants of pathogen, and it also occurs due to the scarcity of plant conservation measures. The disease in maize leaves can be exhibited by varied symptoms; however, it might be complex for farmers to identify the disease in naked eye. Therefore, this paper intends to present a new automatic system for identifying and diagnosing maize leaf diseases. The proposed model includes two major phases: Proposed Feature Extraction and Classification. The first phase is feature extraction, where the proposed 4D-Local Binary Pattern (4D-LBP) based texture features will be extracted. More particularly, Dimension 1 insists pixel intensity, dimension 2 insists angle, dimension 3 insists local frequency from intensity patch and dimension 4 insists global frequency as well. Once the features get extracted, they are subjected for classification process, where the optimized Convolutional Neural Network (CNN) is used, where the count of convolutional layers is optimally tuned. For this optimal selection, a new Adaptive Opposition based Spider Monkey optimization (AOSMO), which is the enhanced version of SMO algorithm. At last, the performance of proposed work is evaluated over other traditional models with respect to accuracy.

Micro-Expressions (MEs) are one kind of facial movement which is very spontaneous and involuntary in nature. MEs are observed when a person attempts to hide or conceal the experiencing emotion in a high-stakes environment. The duration of ME is very short and approximately less than 500 milliseconds. Recognition of such kinds of expressions from lengthy video consequences to a limited Micro Expression Recognition Performance and also creates the computational burden. Hence, in this paper, we propose a new ME spotting (detection of ME frames) method based on a new texture descriptor called Composite Binary Pattern (CBP). As a pre-processing, we employ the viola jones algorithm for landmark regions detection followed by landmark points detection for facial alignment. Next, every aligned face is described through CBP and subjected to feature difference analysis followed by the threshold for ME spotting. For simulation, the REVIEW dataset is used and the performance is measured through Recall, Precision, and F-Score.

Electrocardiography (ECG) signal recognition is one of the popular research topics for machine learning. In this paper, a novel transformation called tower graph transformation is proposed to classify ECG signals with high accuracy rates. It employs a tower graph, which uses minimum, maximum and average pooling methods altogether to generate novel signals for the feature extraction. In order to extract meaningful features, we presented a novel one-dimensional hexadecimal pattern. To select distinctive and informative features, an iterative ReliefF and Neighborhood Component Analysis (NCA) based feature selection is utilized. By using these methods, a novel ECG signal classification approach is presented. In the preprocessing phase, tower graph-based pooling transformation is applied to each signal. The proposed one-dimensional hexadecimal adaptive pattern extracts 1536 features from each node of the tower graph. The extracted features are fused and 15,360 features are obtained and the most discriminative 142 features are selected by the ReliefF and iterative NCA (RFINCA) feature selection approach. These selected features are used as an input to the artificial neural network and deep neural network and 95.70% and 97.10% classification accuracy was obtained respectively. These results demonstrated the success of the proposed tower graph-based method.

With the pervasiveness of secured biometric authentication applications, the fingerprint-based identification system has fascinated much attention recently. However, the major detriment is their recognition sensors are vulnerable to presentation or spoofing attacks from fake fingerprint artifacts. To resolve these issues, a viable anti-deception countermeasure known as presentation attack detection (PAD) mechanism is developed. As handcrafted feature-based classification techniques exhibit encouraging results in computer vision, they are widely employed in fingerprint spoof detection. Notably, the single-feature-based techniques do not perform uniformly over different spoofing and sensing technologies. In this research work, we expound a new hybrid fingerprint presentation attack detection approach (HyFiPAD) that discriminates live and fake fingerprints using majority voting ensemble build on three local and adaptive textural image features. We propose a new descriptor (i.e., a variant of LBP) which is termed as Local Adaptive Binary Pattern (LABP). Thus, the notion of proposed LABP is used to extract more detailed micro-textural features from the fingerprint images. Our LABP features are combined with an existing Complete Local Binary Pattern (CLBP) descriptor to learn two respective SVM classifiers and additionally a sequential model is trained with the manually extracted Binary Statistical Image Features (BSIF). The experiments are performed on benchmark anti-spoofing datasets namely; LivDet 2009, LivDet 2011, LivDet 2013, and LivDet 2015, where an average classification error rate (ACER) of 4.11, 3.19, 2.88, and 2.97% is, respectively, achieved. The overall experimental analysis of the HyFiPAD demonstrates superiority against majority of the state-of-the-art methods. In addition, the proposed technique yields a promising performance on cross-database and cross-sensor liveness detection tests, claiming good generalization capability.

The psychological health of a person plays an important role in their daily life activities. The paper addresses depression issues with the machine learning model using facial expressions of the patient. Some research has already been done on visual based on depression detection methods, but those are illumination variant. The paper uses feature extraction using LBP (Local Binary Pattern) descriptor, which is illumination invariant. The Viola-Jones algorithm is used for face detection and SVM (support vector machine) is considered for classification along with the LBP descriptor to make a complete model for depression level detection. The proposed method captures frontal face from the videos of subjects and their facial features are extracted from each frame. Subsequently, the facial features are analyzed to detect depression levels with the post-processing model. The performance of the proposed system is evaluated using machine learning algorithms in MATLAB. For the real-time system design, it is necessary to test it on the hardware platform. The LBP descriptor has been implemented on FPGA using Xilinx VIVADO 16.4. The results of the proposed method show satisfactory performance and accuracy for depression detection comparison with similar previous work.

The main subject of this paper are binary pattern sequences, that is, sequences of the form ( − 1 ) # ( n , A ) where A is a set of strings of 0 s and 1 s, and # ( n , A ) denotes the total number of times patterns from A appear in the binary expansion of n. A sequence is said to be noncorrelated if the corresponding spectral measure is equal to the Lebesgue measure. We show that it is possible to algorithmically verify if a given binary pattern sequence is noncorrelated. As an application, we compute that there are exactly 2272 noncorrelated binary pattern sequences of length ≤4. If we restrict our attention to patterns that do not end with 0 , we put forward a sufficient condition for a pattern sequence to be noncorrelated. We conjecture that this condition is also necessary, and verify this conjecture for lengths ≤5.

This paper presents a novel face image descriptor called Gammadion Binary Pattern of Shearlet Coefficients (GBPSC) for illumination and noise invariant, homogeneous and heterogeneous face recognition. Exploiting the energy concentration property of the Digital Shearlet Transform, an efficient illumination and noise invariant feature extractor has been devised. Finally, inspired by the Gammadion structure, a robust multi-directional local binary pattern named Gammadion Binary Pattern (GBP) has been proposed. GBP is applied on the previously extracted illumination and noise invariant feature map to generate the GBPSC images. Recognition results on Extended Yale B and TUFTS dataset indicate the primacy of the proposed scheme in terms of common feature representation under varying illumination, and modality. Furthermore, the merger of the proposed GBPSC and Convolutional Neural Network (CNN) consistently outperforms other state-of-the art methods.

Depth sensing with single-shot structured light is widely employed for depth measurement of dynamic scenes. For conventional single-shot structured light techniques, the projected pattern is coded with regards to color, shape, and intensity, aiming at assigning each pixel a unique label. However, the features of the projected pattern are extremely susceptible to the surface texture of the measured object, resulting in errors during performing decoding. In this study, a robust and simple binary pattern is proposed for high-accuracy depth sensing. Only two gray values are utilized in the designed pattern, which is insensitive to environmental noise. The pseudo two-dimensional De Bruijn sequence coding method is employed to reduce the size of the decoding window for robust matching. Short slashes are adopted as features of the pattern that greatly simplifies the feature extraction and pattern decoding. Compared with the Kinect and time-of-flight (TOF) depth cameras, our proposed method can acquire high-precision depth in quantitative and qualitative experiments.

Local binary pattern (LBP) is sensitive to noise. Noise-resistant LBP (NRLBP) addresses this problem by thresholding local neighboring pixels into three-valued states (i.e., 0, 1 and uncertain bits) and recovering uncertain bits via an error-correction mechanism. In this paper, we extend NRLBP to deal with color images and propose a robust color image descriptor called Color context binary pattern (CCBP). In CCBP, we employ scale context and neighbor context to progressively correct the encoded bits. First, we encode intra-channel local neighboring pixels into three-valued states in scale space and use majority voting to correct all states across scales. Then, we compute inter-channel color feature distances and correct the uncertain bits via neighboring bit propagation. Finally, we construct the image descriptor by concatenating all histograms based on the corrected binary codes. Experiments on four benchmark databases demonstrate the robustness of CCBP for color image classification under very low signal-to-noise ratio levels.

This paper simply presents a state of the art survey among the machine learning based approaches for the Glioma classification. As Glioma classification is a very challenging task in the field of medical science and this task is well addressed and taken by the fraternity of machine learning experts, who are working day and night to devise automated approaches that can automate this whole process of Glioma accurate classification from the various medical imaging modalities like Magnetic resonance imaging (MRI), Computed tomography (CT) etc. Although present machine learning techniques offers an opportunity to come up with a highly accurate and automated Glioma classification approach, by performing fusion among the various medical imaging modalities as well as utilizing the various features derived from the multi-modality medical imaging data. This paper also proposed an efficient and accurate automated approach of Glioma classification for the comparison analysis. This proposed approach is based on the use of hybrid ensemble learning model and hybrid feature extraction method, which relies on the Discrete wavelet Decomposition (DWD), Central pixel Neighbourhood Binary pattern (CNBP) and GLRLM (Gray level run length Matrix) methods in order to classify the Glioma (type of mostly diagnosed brain tumors) into Low grade Glioma and High grade Glioma from the fused MRI sequences. Improved eXtreme Gradient Boosting classifier is the hybrid ensemble learning model, which is used in this paper for the first time along with the proposed hybrid texture feature extraction method. Further this proposed approach is compared with the already existing state of the art approaches, which are based on the various machine learning classifiers like Support vector machine (SVM), K-Nearest neighbor (KNN), Naive Bayes (NB) etc. and conventional feature extraction methods in order to present a comprehensive and practical comparison study. The proposed approach is evaluated on the balanced large size local dataset consisting of MRI images of low and high grade Glioma collected from the various MRI centers located in Madhya Pradesh, India as well as on the popular global datasets like, BRATS 2013 and BRATS 2015 with various MRI fusion combinations (T1 + T1C + T2 + Flair, T1 + T1C + T2, T1 + T1C + Flair, T1C + T2 + Flair etc.). The proposed approach employing Improved eXtreme Gradient Boosting ensemble model offers highest accuracy of above 90% on the local dataset with the fusion of T1C + T2 + Flair MRI sequences.

A dynamic image has a distinct quantity of object movement from one to another. It can be any object such as a car, person, an object moving from one point X to another point Y. Image consists of a sense of movement. Applications of object tracking are biometrics tracking, AR uses, video surveillance, passage monitoring, vehicle navigation, etc. Challenges in tracking multifaceted objects are fast movement, geometric conversion, blurring, messy background, artifacts, etc. To resolve this problem by merge all small features with nearby texture features. Texture feature describes the plane space and configuration of an area. A mixture of color and texture feature improves the object details and to increase the strength of the object's illustration. In Existing methods such as binary pattern method all object features are removed, so it is difficult to predict the exact pixel movement. The proposed method of improved binary pattern is also tracking the small changes in the pixel difference in one frame to other. Compared with the existing algorithms, IBP method measures the spatial arrangement of local image texture which reduces the overall processing cost and improves the strength of objective image. To track the similarities and difference of the object in each and every frame efficiently and effectively Improved Local Binary Pattern tracking algorithm was proposed. This proposed technique is an effective way to analysis complicated real time situations compared with other methods.

A rapid growth in medical ultrasound database makes it difficult for medical practitioners to manage and search relevant data with good efficiency. Hence, a novel image retrieval technique using Mean Distance Local Binary Pattern (Mean Distance LBP) has been proposed for content-based image retrieval. The conventional local binary pattern (LBP) converts every pixel of image into a binary pattern based on their relationship with neighbourhood pixels. The proposed feature descriptor differs from local binary pattern as it transforms the mutual relationship of all neighbouring pixels in a binary pattern based on their standard deviation templates as well as Euclidean distance from the center pixel. Color feature and Gray Level Co-occurrence Matrix have also been used in this work. To prove the excellence of the proposed method, experiments have been conducted on two different databases of natural images and face images. Further, the method is applied on real time ultrasound database for retrieval of liver images from a set of ultrasound images of various organs. The performance has been observed using well-known evaluation measures, precision and recall, and compared with some state-of-art local patterns. Comparison shows a significant improvement in the proposed method over existing methods.

Local feature descriptor plays a significant role in texture classification. However, in the traditional local binary pattern method, image pixels are converted into a binary pattern based on the relationship between center and neighborhood pixels. This paper introduces a novel feature extraction method named local neighbourhood edge responsive binary pattern to extract and categorize reliable texture features from images for achieving an automated classification of different types of ocean bottom sediments. Initially, the basic magnitude variance pixel values are derived depending on an odd and even pixel value of a 3 × 3 image patch. Further, the edge information is extracted using the local directional pattern method from all images. The edge response of the image is obtained using a kirsch mask in all eight directions. The encoding condition is then applied to both the local intensity and the edge information to create a unique descriptor value. Finally, a new learning algorithm called GMJAYA-ELM combines the Gaussian mutated JAYA (GMJAYA) with an extreme learning machine (ELM) for texture classification. The GMJAYA is used to optimize the input weights and hidden biases of single-hidden-layer feed-forward neural networks. Experimental findings indicate that the suggested method yields better accuracy and sensitivity efficiency among various groups. The proposed algorithm is tested by contrasting outcomes with standard learning schemes such as PSO-ELM, GA-ELM, ABC-ELM, Birdswarm-ELM, and JAYA-ELM, suggesting the dominance of GMJAYA-ELM.

This research paper presents a comparative study between 14 state of art descriptors which includes Local Binary Pattern (LBP), Median Binary Pattern (MBP), 6 × 6 Multiscale Block LBP (6 × 6 MB-LBP), Local Neighborhood Difference Pattern (LNDP), Logically Connected-LBP (LC-LBP), Local Phase Quantization (LPQ), Compound LBP (CLBP), Horizontal Elliptical LBP (HELBP), Vertical Elliptical LBP (VELBP), ELBP, Neighborhood Intensity Based LBP (NI-LBP), Median Robust Extended LBP Based on NI (MRELBP-NI), Radial Difference-LBP (RD-LBP) and Transition LBP (tLBP). For all the descriptors the features are extracted globally and the dimensionality of the feature size is reduced by employing Principal Component Analysis (PCA) and Fishers Linear Discriminant Analysis (FLDA). Finally classification is performed by Support Vector Machines (SVMs) and Nearest Neighbor (NN). Experiments are performed on 8 challenging databases which covers all the major challenges such as pose variations, illumination variations, expression variations and occlusion changes. The 8 challenging databases includes ORL, GT, Faces94, MIT-CBCL, Yale, YB, EYB and SOF. Out of all the descriptors it is the performance of the CLBP descriptor which is most encouraging. On some occasions the MRELBP-NI descriptor also achieves good results. But all in all the CLBP descriptor achieves the best results. In addition to this Deep learning based descriptors are also discussed in the paper.

Annotations of character IDs in news images are critical as ground truth for news retrieval and recommendation system. Universality and accuracy optimization of deep neural network models constitutes the key technology to improve the precision and computing efficiency of automatic news character identification, which is attracting increased attention globally. This paper explores the optimized deep neural network model for automatic focus personage identification in multi-lingual news. First, the face model of the focus personage is trained by using the corresponding face images from German news as positive samples. Next, the scheme of Recurrent Convolutional Neural Network (RCNN) + Bi-directional Long-Short Term Memory (Bi-LSTM) + Conditional Random Field (CRF) is utilized to label the focus name, and the RCNN-RCNN encoder–decoder is applied to translate names of people into multiple languages. Third, face features are described by combining the advantages of Local Gabor Binary Pattern Histogram Sequence (LGBPHS) and RCNN, and iterative quantization (ITQ) is used to binarize codes. Finally, a name semantic network is built for different domains. Experiments are performed on a dataset which comprises approximately 100,000 news images. The experimental results demonstrate that the proposed method achieves a significant improvement over other algorithms.

Local binary pattern (LBP) and its many variants have shown effectiveness for texture classification. However, most of these LBP methods focus on encoding local intensity differences between a central pixel and its neighboring sampling points and consequently have two major problems: 1) they are unable to describe the intensity order relationships among neighboring sampling points, and 2) they fail to capture long-range pixel interactions that take place outside a compact neighborhood. In view of these problems, in this paper we propose two novel operators, called local grouped order pattern (LGOP) and non-local binary pattern (NLBP), for texture description. For the first problem, LGOP groups the neighboring sampling points by referring to a dominant direction and encodes the groupwise intensity order relationships. For the second problem, NLBP computes several anchors based on global image statistics and progressively encodes non-local intensity differences between the neighboring sampling points and anchors. Finally, we combine LGOP and NLBP via central pixel encoding to construct discriminative histogram features as texture descriptor LGONBP. Experiments on four texture benchmark databases (i.e., Outex, CUReT, UMD and KTH-TIPS) demonstrate the superiority of LGONBP over state-of-the-art LBP variants for texture classification under both noise-free and noisy conditions. The code is available at https://github.com/stc-cqupt/LGONBP .

Introduction: COVID-19 is a pandemic disease affecting the global mankind since December 2019 Diagnosing COVID-19 using lung X-ray image is a great challenge faced by the entire world Early detection helps the doctors to suggest suitable treatment and also helps speedy recovery of the patients Advancements in the field of computer vision aid medical practitioners to predict and diagnosis disease accurately Objective: This study aims to analyze the chest X-ray for determining the presence of COVID-19 using machine learning algo-rithm Methods: Researchers propose various techniques using machine learning algorithms and deep learning approaches to de-tect COVID-19 However, obtaining an accurate solution using these AI techniques is the main challenge still remains open to researchers Results: This paper proposes a Local Binary Pattern technique to extract discriminant features for distinguishing COVID-19 disease using the X-ray images The extracted features are given as input to various classifiers namely Random Forest (RF), Linear Discriminant Analysis (LDA), k-Nearest Neighbour (kNN), Classification and Regression Trees (CART), Support Vector Machine (SVM), Linear Regression (LR), and Multi-layer perceptron neural network (MLP) The proposed model has achieved an accuracy of 77 7% from Local Binary Pattern (LBP) features coupled with Random Forest classifier Conclusion: The proposed algorithm analyzed COVID X-ray images to classify the data in to COVID-19 or not The features are extracted and are classified using machine learning algorithms The model achieved high accuracy for linear binary pattern with random forest classifier © IJCRR

Multitarget datasets that correlate bioactivity landscapes of small-molecules toward different related or unrelated pharmacological targets are crucial for novel drug design and discovery. ABC transporters are critical membrane-bound transport proteins that impact drug and metabolite distribution in human disease as well as disease diagnosis and therapy. Molecular-structural patterns are of the highest importance for the drug discovery process as demonstrated by the novel drug discovery tool ‘computer-aided pattern analysis’ (‘C@PA’). Here, we report a multitarget dataset of 1,167 ABC transporter inhibitors analyzed for 604 molecular substructures in a statistical binary pattern distribution scheme. This binary pattern multitarget dataset (ABC_BPMDS) can be utilized for various areas. These areas include the intended design of (i) polypharmacological agents, (ii) highly potent and selective ABC transporter-targeting agents, but also (iii) agents that avoid clearance by the focused ABC transporters [e.g., at the blood-brain barrier (BBB)]. The information provided will not only facilitate novel drug prediction and discovery of ABC transporter-targeting agents, but also drug design in general in terms of pharmacokinetics and pharmacodynamics., A detailed curation protocol and the related GraphPad Prism file containing the curated concentration-effect curves for the new binary pattern multitarget dataset (ABC_BPMDS); freely available under the https://doi.org/10.5281/zenodo.6384343.

Multitarget datasets that correlate bioactivity landscapes of small-molecules toward different related or unrelated pharmacological targets are crucial for novel drug design and discovery. ABC transporters are critical membrane-bound transport proteins that impact drug and metabolite distribution in human disease as well as disease diagnosis and therapy. Molecular-structural patterns are of the highest importance for the drug discovery process as demonstrated by the novel drug discovery tool ���computer-aided pattern analysis��� (���C@PA���). Here, we report a multitarget dataset of 1,167 ABC transporter inhibitors analyzed for 604 molecular substructures in a statistical binary pattern distribution scheme. This binary pattern multitarget dataset (ABC_BPMDS) can be utilized for various areas. These areas include the intended design of (i) polypharmacological agents, (ii) highly potent and selective ABC transporter-targeting agents, but also (iii) agents that avoid clearance by the focused ABC transporters [e.g., at the blood-brain barrier (BBB)]. The information provided will not only facilitate novel drug prediction and discovery of ABC transporter-targeting agents, but also drug design in general in terms of pharmacokinetics and pharmacodynamics., An easy-to-use sort function allows the user to discriminate the compounds regarding their bioactivities toward the targets, physicochemical properties, or molecular-structural features, but also in terms of the 604 different substructures. Hence, the user can retrieve the necessary binary pattern information for subsequent virtual screening and rational drug design approaches. In addition, the ABC_BPMDS table provides a useful framework for similar data mining approaches, taking, for example, different mode of modulations (e.g., activation), different bioactivity measurements [e.g., in vitro (ATPase assays or MDR reversal assays) and in silico (e.g., structure-computational binding mode analyses by, for example molecular docking or molecular dynamics simulations)], or different pharmacological targets [e.g. under-studied human/bacterial ABC transporters, G-protein coupled receptors (GPCRs), ion channels, solute carriers (SLCs; PANSLC, http://www.panslc.info) or tyrosine kinase inhibitors (TKIs)] into account.