Co-occurrence Local Binary Pattern Research Articles

2D-LCoLBP: A Learning Two-Dimensional Co-Occurrence Local Binary Pattern for Image Recognition.

The rotation, scale and translation invariance of extracted features have a high significance in image recognition. Local binary pattern (LBP) and LBP-based descriptors have been widely used in image recognition due to feature discrimination and computational efficiency. However, most of the existing LBP-based descriptors have been designed to achieve rotation invariance while fail to achieve scale invariance. Moreover, it is usually difficult to achieve a good trade-off between the feature discrimination and the feature dimension. In this work, a learning 2D co-occurrence LBP termed 2D-LCoLBP is proposed to address these issues. Firstly, a weighted joint histogram is constructed in different neighborhoods and scales of an image to represent the multi-neighborhood and multi-scale LBP (2D-MLBP) and achieve the rotation invariance. A feature learning strategy is then designed to learn the compact and robust descriptor (2D-LCoLBP) from LBP pattern pairs across different scales in the extracted 2D-MLBP to characterize the most stable local structures and achieve the scale invariance, as well as decrease the feature dimension and improve the noise robustness. Finally, a linear SVM classifier is employed for recognition. We applied the proposed 2D-LCoLBP on four image recognition tasks-texture, object, face and food recognition with ten image databases. Experimental results show that 2D-LCoLBP has obviously low feature dimension but outperforms the state-of-the-art LBP-based descriptors in terms of recognition accuracy under noise-free, Gaussian noise and JPEG compression conditions.

A Novel Image Processing Technique for Analyzing Wear Worn Surface Roughness and Corrosion Behavior of Sintered Mg/B4C Composites

The pure Mg and Mg-B4C composites are produced with dissimilar weight percentages (Mg-5% B4C, Mg-10% B4C, and Mg-15% B4C) using powder metallurgy methods. To find the consequences of B4C in pure Mg, the Vickers micro-hardness, dry sliding wear, 3D roughness profile, and immersion corrosion tests were conducted. To quantify the texture of the composite surface, the local binary co-occurrence pattern quantification technique was used in this analysis and the related texture features were extracted to evaluate the composite surfaces. The wear analysis of Mg-B4C composites was examined in disparate loads of 30, 60, and 90 N with a constant sliding velocity of 2 m/s and in 2000 m constant sliding distance. The changes in wear rate, wear depth, and friction coefficient were compiled and analyzed. It was identified that the wear rate increased with the increment of load. To comprehend the wear mechanism of pure Mg and all Mg-B4C (5–15%) composites, its worn surfaces were subjected to SEM and EDS mapping analyses. The surface roughness of the worn surfaces was explored using a 3D roughness profile test and atomic force microscopy analysis. The obtained results exhibited that the hardness of Mg-B4C composites increased with increasing of B4C weight percentages. The rate of corrosion was discovered using the weight loss method. The corrosion rate decreased to a great extent with the increasing weight percentage of B4C in the Mg matrix.

Elliptical local binary co-occurrence pattern for face image retrieval

Face image retrieval is considered as an active research field in computer vision. This paper describes a novel feature descriptor called elliptical local binary co-occurrence pattern (ELBCoP) for face image retrieval. This descriptor employs a combination of sparse elliptical local binary pattern (ELBP) and grey level co-occurrence matrix (GLCM). Unlike previous approaches, the sparse ELBP consider only 4 horizontal and 4 vertical neighbors from the elliptic shaped neighborhood, and uses 4 simple diagonal neighbors. Then the co-occurrence of pixel pairs, in the three local pattern maps obtained using sparse ELBP, are calculated using GLCM in two selected directions. These two directions of GLCM are selected according to the shape of the elliptic neighborhood. Through GLCM, the proposed ELBCoP extracts the frequency details and also the spatial relationship between the local pattern pairs very effectively. It is also shown that a properly selected subset of ELBCoP can still provide a robust and efficient description of face images, with significant reduction in feature vector dimensions. Experimental results on two benchmark face image databases show superior retrieval performance of ELBCoP and also subset of ELBCoP, when compared to various state of the art methods including a few very recent ones.

Writer identification using texture features: A comparative study

A texture-based approach for writer identification of multiple scripts on a single platform is presented in this paper. Potential texture descriptors, namely Local Binary Pattern (LBP), Local Phase Quantization (LPQ), Discrete Wavelet Transform-based Local Extrema Pattern (DWT+LEP), Discrete Wavelet Transform-based Directional and Local Extrema Pattern (DWT+DLEP), Center Symmetric Local Binary Co-occurrence Pattern (CSLBCoP), and Local Tri-Directional Pattern (LTriDP) have been analyzed for identifying the writers. Comparative study for Latin, Arabic, and Devnagri databases was performed, with the Devnagri database contributed by us. The study shows high writer identification rates of 97.62% for IAM dataset using LBP features and Support Vector Machine (SVM) classifier, 95.60% for KHATT database using k-Nearest Neighbor (kNN), and 65.80% for Devnagri scripts using LPQ features and kNN classifier.

Automatic Malware Classification via PRICoLBP

Creating effective features is a critical issue in malware analysis. It requires a proper tradeoff between discriminative power and invariance. Previous studies have shown that it is fairly effective to design features based on the binary code. However, the current existing binary-based features seldom take into consideration the problem of obfuscation, such as relocated sections, incomplete code and redundant operations. In this paper, we propose a novel Pairwise rotation invariant co-occurrence local binary pattern (PRICoLBP) feature, and further extend it to incorporate the Term frequency-inverse document frequency (TFIDF) transform. Different from other static analysis techniques, our method not only achieves better linear separability, but also appears to be more resilient to obfuscation. In addition, we evaluate PRICoLBPTFIDF comprehensively on three datasets from different perspectives, e.g., classification performance, classifier selection and performance against obfuscation. What's more, we compare our PRICoLBP-TFIDF method with other techniques, and demonstrate that PRICoLBP-TFIDF is quite an efficient and effective tradeoff between discriminative power and invariance.

Mixed co-occurrence of local binary patterns and Hamming-distance-based local binary patterns

Local binary patterns (LBP) have powerful discriminative capabilities. However, traditional methods with LBP histograms cannot capture spatial structures of LBP codes. To extract the spatial structures of an LBP code map, we compute and encode the Hamming distances between LBP codes of a center point and its neighbors on the LBP code map to generate a new code, which is called Hamming-distance-based local binary patterns (HDLBP). Then, we calculate a joint histogram of LBP and HDLBP to represent the LBP co-occurrence with HDLBP (LBPCoHDLBP). Circular bit-wise shift techniques are used to align HDLBP with LBP for rotation invariance. To achieve scale invariance, we extract the feature of LBPCoHDLBP from each scale and concatenate all features of different scales. Finally, we use the sum of absolute differences (SAD) between the intensities of the center point and its neighbors to weight LBPCoHDLBP for further improvement. Extensive experiments show that our method achieves better performance for smoke detection, texture classification and material recognition than most existing methods and is more computationally efficient.

A decision support system for type 1 diabetes mellitus diagnostics based on dual channel analysis of red blood cell membrane fluidity

Background and objective: Investigation of membrane fluidity by metabolic functional imaging opens up a new and important area of translational research in type 1 diabetes mellitus, being a useful and sensitive biomarker for disease monitoring and treatment. We investigate here how data on membrane fluidity can be used for diabetes monitoring.Methods: We present a decision support system that distinguishes between healthy subjects, type 1 diabetes mellitus patients, and type 1 diabetes mellitus patients with complications. It leverages on dual channel data computed from the physical state of human red blood cells membranes by means of features based on first- and second-order statistical measures as well as on rotation invariant co-occurrence local binary patterns. The experiments were carried out on a dataset of more than 1000 images belonging to 27 subjects.Results: Our method shows a global accuracy of 100%, outperforming also the state-of-the-art approach based on the glycosylated hemoglobin.Conclusions: The proposed recognition approach permits to achieve promising results.

Handcrafted vs. non-handcrafted features for computer vision classification

This work presents a generic computer vision system designed for exploiting trained deep Convolutional Neural Networks (CNN) as a generic feature extractor and mixing these features with more traditional hand-crafted features. Such a system is a single structure that can be used for synthesizing a large number of different image classification tasks. Three substructures are proposed for creating the generic computer vision system starting from handcrafted and non-handcrafter features: i)one that remaps the output layer of a trained CNN to classify a different problem using an SVM; ii) a second for exploiting the output of the penultimate layer of a trained CNN as a feature vector to feed an SVM; and iii) a third for merging the output of some deep layers, applying a dimensionality reduction method, and using these features as the input to an SVM. The application of feature transform techniques to reduce the dimensionality of feature sets coming from the deep layers represents one of the main contributions of this paper. Three approaches are used for the non-handcrafted features: deep transfer learning features based on convolutional neural networks (CNN), principal component analysis network (PCAN), and the compact binary descriptor (CBD). For the handcrafted features, a wide variety of state-of-the-art algorithms are considered: Local Ternary Patterns, Local Phase Quantization, Rotation Invariant Co-occurrence Local Binary Patterns, Completed Local Binary Patterns, Rotated local binary pattern image, Globally Rotation Invariant Multi-scale Co-occurrence Local Binary Pattern, and several others. The computer vision system based on the proposed approach was tested on many different datasets, demonstrating the generalizability of the proposed approach thanks to the strong performance recorded. The Wilcoxon signed rank test is used to compare the different methods; moreover, the independence of the different methods is studied using the Q-statistic. To facilitate replication of our experiments, the MATLAB source code will be available at (https://www.dropbox.com/s/bguw035yrqz0pwp/ElencoCode.docx?dl=0).

Automatic detection and classification of leukocytes using convolutional neural networks.

The detection and classification of white blood cells (WBCs, also known as Leukocytes) is a hot issue because of its important applications in disease diagnosis. Nowadays the morphological analysis of blood cells is operated manually by skilled operators, which results in some drawbacks such as slowness of the analysis, a non-standard accuracy, and the dependence on the operator's skills. Although there have been many papers studying the detection of WBCs or classification of WBCs independently, few papers consider them together. This paper proposes an automatic detection and classification system for WBCs from peripheral blood images. It firstly proposes an algorithm to detect WBCs from the microscope images based on the simple relation of colors R,B and morphological operation. Then a granularity feature (pairwise rotation invariant co-occurrence local binary pattern, PRICoLBP feature) and SVM are applied to classify eosinophil and basophil from other WBCs firstly. Lastly, convolution neural networks are used to extract features in high level from WBCs automatically, and a random forest is applied to these features to recognize the other three kinds of WBCs: neutrophil, monocyte and lymphocyte. Some detection experiments on Cellavison database and ALL-IDB database show that our proposed detection method has better effect almost than iterative threshold method with less cost time, and some classification experiments show that our proposed classification method has better accuracy almost than some other methods.

Global Anomaly Crowd Behavior Detection Using Crowd Behavior Feature Vector

In the area of crowd abnormal detection, the parameter of population density, is seldom used to the global crowd behavior detection. Some of the references simply use the LBP or spatial-temporal LBP features to fulfill the abnormal detection. They don’t make full use of the crowd density characteristics and dynamic characteristics. This paper proposes a novel method by increasing the dimension of feature vector to increase the information content so as to improve the recognition accuracy. That is to say the crowd dynamic information and crowd density information will be combined together to form a higher dimension of feature vectors, which is named as the crowd behavior feature vector in this paper to improve the robustness of the algorithm. Finally, Support Vector Machines (SVM) is adopted to detect the abnormal events using the crowd behavior feature vector. This work utilizes the Local Binary Pattern Co-Occurrence Matrix (LBPCM) for crowd density estimation to ensure the excellent accuracy. At the same time, it adopts high accuracy optical flow histograms of the orientation of interaction force to extract the crowd dynamic information (HOIF). After verification, we discovered this algorithm not only can get the good discrimination on the benchmark dataset UMN, but also can achieve the pretty high recognition rate about the web dataset.

Center symmetric local binary co-occurrence pattern for texture, face and bio-medical image retrieval

Content based image retrieval is a common problem for a large image database. Many methods have been proposed for image retrieval for some particular type of datasets. In the proposed work, a new image retrieval technique has been introduced. This technique is useful for different kind of dataset. In the proposed method, center symmetric local binary pattern has been extracted from the original image to obtain the local information. Co-occurrence of pixel pairs in local pattern map have been observed in different directions and distances using gray level co-occurrence matrix. Earlier methods have utilized histogram to extract the frequency information of local pattern map but co-occurrence of pixel pairs is more robust than frequency of patterns. The proposed method is tested on three different category of images, i.e., texture, face and medical image database and compared with typical state-of-the-art local patterns.

Globally rotation invariant multi-scale co-occurrence local binary pattern

This paper proposes a globally rotation invariant multi-scale co-occurrence local binary pattern (MCLBP) feature for texture-relevant tasks. In MCLBP, we arrange all co-occurrence patterns into groups according to properties of the co-patterns, and design three encoding functions (Sum, Moment, and Fourier Pooling) to extract features from each group. The MCLBP can effectively capture the correlation information between different scales and is also globally rotation invariant (GRI). The MCLBP is substantially different from most existing LBP variants including the LBP, the CLBP, and the MSJ-LBP that achieves rotation invariance by locally rotation invariant (LRI) encoding. We fully evaluate the properties of the MCLBP and compare it with some powerful features on five challenging databases. Extensive experiments demonstrate the effectiveness of the MCLBP compared to the state-of-the-art LBP variants including the CLBP and the LBPHF. Meanwhile, the dimension and computational cost of the MCLBP is also lower than that of the CLBP_S/M/C and LBPHF_S_M.

Infrared face recognition based on LBP co-occurrence matrix and partial least squares

Infrared face recognition, being light-independent, and not vulnerable to facial skin, expressions and posture, can avoid or limit the drawbacks of face recognition in visible light. This paper proposes an infrared face recognition method based on Local Binary Pattern (LBP) co-occurrence matrix. In traditional LBP-based features such as LBP histogram, space locations information, which is an important feature for recognition, is discarded. Considering such spatial relations in infrared faces, we introduce co-occurrence matrix of LBP codes, instead of histogram, to obtain more discriminative representation for location features. To cope with the high dimensions of LBP co-occurrence matrix, the final classifier formulates Partial Least Squares (PLS) regression for accurate classification. The experimental results show combination of LBP co-occurrence matrix and PLS achieve better infrared face recognition performance compared to state-of-the-art approaches.

Pairwise Rotation Invariant Co-Occurrence Local Binary Pattern.

Designing effective features is a fundamental problem in computer vision. However, it is usually difficult to achieve a great tradeoff between discriminative power and robustness. Previous works shown that spatial co-occurrence can boost the discriminative power of features. However the current existing co-occurrence features are taking few considerations to the robustness and hence suffering from sensitivity to geometric and photometric variations. In this work, we study the Transform Invariance (TI) of co-occurrence features. Concretely we formally introduce a Pairwise Transform Invariance (PTI) principle, and then propose a novel Pairwise Rotation Invariant Co-occurrence Local Binary Pattern (PRICoLBP) feature, and further extend it to incorporate multi-scale, multi-orientation, and multi-channel information. Different from other LBP variants, PRICoLBP can not only capture the spatial context co-occurrence information effectively, but also possess rotation invariance. We evaluate PRICoLBP comprehensively on nine benchmark data sets from five different perspectives, e.g., encoding strategy, rotation invariance, the number of templates, speed, and discriminative power compared to other LBP variants. Furthermore we apply PRICoLBP to six different but related applications-texture, material, flower, leaf, food, and scene classification, and demonstrate that PRICoLBP is efficient, effective, and of a well-balanced tradeoff between the discriminative power and robustness.