Comparative Study on Local Binary Patterns for Mammographic Density and Risk Scoring.

Local line directional pattern for palmprint recognition

Biometrics is the study of methods of recognizing humans based on their behavioral and physical characteristics or traits. Face recognition is one of the biometric modalities that received a great amount of attention from many researchers during the past few decades because of its potential applications in a variety of security domains. Face recognition however is not only concerned with recognizing human faces, but also with recognizing faces of non-biological entities or avatars. Fortunately, the need for secure and affordable virtual worlds is attracting the attention of many researchers who seek to find fast, automatic and reliable ways to identify virtual worlds’ avatars. In this work, I propose new techniques for recognizing avatar faces, which also can be applied to recognize human faces. Proposed methods are based mainly on a well-known and efficient local texture descriptor, Local Binary Pattern (LBP). I am applying different versions of LBP such as: Hierarchical Multi-scale Local Binary Patterns and Adaptive Local Binary Pattern with Directional Statistical Features in the wavelet space and discuss the effect of this application on the performance of each LBP version. In addition, I use a new version of LBP called Local Difference Pattern (LDP) with other well-known descriptors and classifiers to differentiate between human and avatar face images. The original LBP achieves high recognition rate if the tested images are pure but its performance gets worse if these images are corrupted by noise. To deal with this problem I propose a new definition to the original LBP in which the LBP descriptor will not threshold all the neighborhood pixel based on the central pixel value. A weight for each pixel in the neighborhood will be computed, a new value for each pixel will be calculated and then using simple statistical operations will be used to compute the new threshold, which will change automatically, based on the pixel’s values. This threshold can be applied with the original LBP or any other version of LBP and can be extended to work with Local Ternary Pattern (LTP) or any version of LTP to produce different versions of LTP for recognizing noisy avatar and human faces images.

Osteoarthritis (OA) causes progressive degeneration of articular cartilage and pathological changes in subchondral bone. These changes can be assessed volumetrically using micro-computed tomography (\(\mu \)CT) imaging. The local descriptor, i.e. local binary pattern (LBP), is a new alternative solution to perform analysis of local bone structures from \(\mu \)CT scans. In this study, different trabecular bone samples were prepared from patients diagnosed with OA and treated with total knee arthroplasty. The LBP descriptor was applied to correlate the distribution of local patterns with the severity of the disease. The results obtained suggest the appearance and disappearance of specific oriented patterns with OA, as an adaptation of the bone to the decrease of cartilage thickness. The experimental results suggest that the LBP descriptor can be used to assess the changes in the trabecular bone due to OA.

In this paper, local directional position pattern (LDPP) and local ternary pattern (LTP) are selected for facial recognition method which are having many advantages over previous techniques like local binary pattern (LBP) and local directional pattern (LDP). The selected techniques of LDPP and LTP are estrangement in their algorithms which help solely to extract features out of an image. LDPP is a revised form of LDP. In a typical LDP, only the top edge direction was taken into consideration, but strength sign of the pixel was not considered which may result in same code for opposite kind of edge pixel. This snag is overcome by LDPP which is further concatenated with LTP for better feature extraction. Once features are extracted they are trained using deep belief network. In the experimental work 10 images of each expression i.e. angry, surprise, disgust, neutral, sad, smile are selected. LDPP and LTP are concatenated followed by principal component analysis (PCA) and general discriminant analysis (GDA). Further for training, Deep Belief Network (DBN) is used which eventually increases the recognition rate and achieve accuracy of 95.3% which was 89.3% without concatenating.

This paper proposes a novel approach in image compression based on Local Binary Pattern (LBP). LBP has already been used as a simple texture descriptor, labeling the image pixels by looking at the points surrounding a central point (usually on a 3×3 neighborhood) and examining whether these neighbors' color values are greater or less than the central point and accordingly assigning a binary value to the corresponding bit. The description of image's local pattern results in an eight-bit binary description, but in order to restore the image from such a LBP description, the value of each central pixel is also needed. These two pieces of information, i.e. the LBP description and the actual original value for each local neighborhood central pixel, are stored in a newly proposed Local Binary Compressed format, denoted .LBC, from which the image can be reconstructed by employing statistical methods, i.e. generating smaller or larger sets of random numbers to fill in the missing information within each local neighborhood, based on the LBP descriptor. Two statistical distributions were tested and, apart from the compression performance, a Structural Similarity Index Metric was used to evaluate the results.

The Local Binary Pattern (LBP) and its variants are capable of extracting image texture and have been successfully applied to classification. However, LBP has not been used to extract and describe the texture of polarized images, and simple LBP cannot characterize the polarized texture information from different polarizations of angles. In order to solve these problems, we propose a new multi-angle orthogonal difference polarization image texture descriptor (MODP_ITD) by analyzing the relationship between the difference of orthogonal difference polarization images from different angles and the pixel intensity distribution in the local neighborhood of images from different angles. The MODP_ITD consists of three patterns: multi-angle polarization orthogonal difference local binary pattern (MODP_LBP), multi-angle polarization orthogonal difference local sampling point principal component sequence pattern (MODP_LPCSP) and multi-angle orthogonal difference polarization local difference binary pattern (MODP_LDBP). The MODP_LBP extracts local corresponding texture characteristics of polarized orthogonal difference images from multiple angles. The MODP_LPCSP sorts the principal component order of each angle orthogonal difference local sampling point. The MODP LDBP extracts the local difference characteristics between different angles by constructing a new polarized image. Then, the frequency histograms of MODP_LBP, MOD_LPCSP ,and MODP_LDBP are cascaded to generate MODP_ITD, so as to distinguish local neighborhoods. By using vertical and parallel polarization and unpolarized light active illumination, combined with the measurements at three different detection zenith angles, we constructed a polarization texture image database. A substantial number of experimental results on the self-built database show that our proposed MODP_ITD can represent the detailed information of polarization images texture. In addition, compared with the existing LBP methods, The MODP_ITD has a competitive advantage in classification accuracy.

The texture is an essential property of an image that tells about the arrangements of pixels of different intensities in an image matrix. Researchers have used many descriptors for texture classification of the images in computer vision and image processing. Local Binary Pattern (LBP) is one of the descriptors, which is computationally efficient and straightforward for texture classification. In recent years, researchers have made many enhancements to the basic LBP method to improve the quality of extracted features for an image. This paper presents a Local Roughness Binary Pattern (LRBP) descriptor for feature extraction of an image to increase LBP discriminative capability. This new descriptor extracts features from the local region of an image called a partial feature vector and concatenates these local features to obtain a feature vector of an image. This feature vector is used as input to a classifier to classify the class of an image. We tested the proposed descriptor for the images available on CUReT and KTHTIPS2b databases with Support Vector Machine (SVM) and k-nearest Neighbors (KNN) classifiers. We have presented the experimental results obtained from the analysis process regarding classification accuracy and confusion matrix. Finally, we have presented a performance comparison between the proposed method, basic LBP descriptor, and its variants. Results show that the proposed descriptor gives much better results than other descriptors on the KTHTIPS2b Database when we use the KNN classifier.KeywordsLBPLocal binary patternLRBPLocal roughness binary patternSVMSupport vector machineKNN K-nearest neighbors

This study focuses on a comprehensive study of Automatic Signature Verification (ASV) for off-line Thai signatures; an investigation was carried out to characterise the challenges in Thai ASV and to baseline the performance of Thai ASV employing baseline features, being Local Binary Pattern, Local Directional Pattern, Local Binary and Directional Patterns combined (LBDP), and the baseline shape/feature-based hidden Markov model. As there was no publicly available Thai signature database found in the literature, the authors have developed and proposed a database considering real-world signatures from Thailand. The authors have also identified their latent challenges and characterised Thai signature-based ASV. The database consists of 5,400 signatures from 100 signers. Thai signatures could be bi-script in nature, considering the fact that a single signature can contain only Thai or Roman characters or contain both Roman and Thai, which poses an interesting challenge for script-independent SV. Therefore, along with the baseline experiments, the investigation on the influence and nature of bi-script ASV was also conducted. From the equal error rates and Bhattacharyya distance, the score achieved in the experiments indicate that the Thai SV scenario is a script-independent problem. The open research area on this subject of research has also been addressed.

A variety of binary feature descriptors such as local binary pattern (LBP) and its variations have recently attracted considerable attention for modelling backgrounds, due to their robustness and strong discriminatory power. However, most existing binary feature descriptors fail to model complex scenes due to their sensitivity to noise. In this paper, we propose an effective local compact binary descriptor for background modelling. For each image, local compact binary patterns (LCBPs) are first extracted by computing a number of low-dimensional pixel difference vectors (PDVs). Then, the LCBP is extended to the spatiotemporal domain taking into account the temporal persistence of pixels, and a novel local compact binary descriptor, STLCBP, is proposed. Multiple color spaces are also considered in order to separate foreground from background pixels accurately. Finally, a joint domain-range adaptive kernel density estimate (KDE) model is used to estimate the background and foreground scores by combining texture features with color features. Experimental results on two well-known datasets, I2R and CDnet2014, demonstrate that the proposed approach significantly outperforms many state-of-the-art methods and works effectively on a wide range of complex videos.

This paper proposes a novel high-order local pattern descriptor, local derivative pattern (LDP), for face recognition. LDP is a general framework to encode directional pattern features based on local derivative variations. The n(th)-order LDP is proposed to encode the (n-1)(th) -order local derivative direction variations, which can capture more detailed information than the first-order local pattern used in local binary pattern (LBP). Different from LBP encoding the relationship between the central point and its neighbors, the LDP templates extract high-order local information by encoding various distinctive spatial relationships contained in a given local region. Both gray-level images and Gabor feature images are used to evaluate the comparative performances of LDP and LBP. Extensive experimental results on FERET, CAS-PEAL, CMU-PIE, Extended Yale B, and FRGC databases show that the high-order LDP consistently performs much better than LBP for both face identification and face verification under various conditions.

The evaluation of osteoporotic disease from X-ray images presents a major challenge for pattern recognition and medical applications. Textured images from the bone microarchitecture of osteoporotic and healthy subjects show a high degree of similarity, thus drastically increasing the difficulty of classifying such textures. In this paper, we propose a new method to separate osteoporotic cases from healthy controls, using texture analysis. The idea consists in combining global and local information to better capture the image characteristics. Global information is characterized by image projection which conveys information about the global aspect of the texture. Local information is encoded by the local patterns using neighborhood operators. The proposed technique is based on the local binary pattern (LBP) descriptor which has been classically applied on two dimensional (2D) images. Our algorithm is a derived solution for the 1D projected fields of the 2D images. Experiments were conducted on two populations of osteoporotic patients and control subjects. Compared to the classical LBP, the proposed approach yields a better classification rate of the two populations.

Automatic image annotation is the process of providing tags to salient objects in the image. The aim is achieved by first identifying salient objects. For this, the traditional gradient vector flow (GVF) model is modified to incorporate saliency by adding minimum directional contrast to the data part in the energy functional of GVF. To provide tags, three features: color, local binary pattern and local direction pattern are used. Classification is done by modifying cluster-based multi-label learning with feature-induced labeling information enrichment (C-MLFE) and is termed as C’-MLFE. This involves clustering the training data into two sets. For each cluster, a squared weight matrix records the influence of each instance on the other. This relationship among the training instance is used to enrich the labeling information of the test set. The result is compared with six state-of-the-art algorithms.

Automated recognition of facial expression has attracted significant attention in recent years due to its potential applicability in security and surveillance, human computer interaction, social robotics, and animation. This paper presents a new facial expression recognition method that utilizes bit plane specific local image description in a weighted score level fusion. The motivation is to utilize bit plane slicing to highlight the contribution of a particular bit plane made to the holistic facial appearance, which is then used in a weighted score level fusion in order to boost the recognition performance. A new local image descriptor is proposed specifically to extract local features from bit plane representations that utilizes Fisher linear discriminant to maximize the inter-class distance, while minimizing the intra-class variance. Two well-known facial expression databases, namely the Cohn-Kanade (CK) and the Japanese female facial expression (JAFFE) database have been used to evaluate the performance of the proposed method against existing facial appearance descriptors, such as local binary pattern (LBP), local ternary pattern (LTP), local directional pattern (LDP), and linear discriminant analysis (LDA). Experiments with a total of seven prototypic facial expressions show promising results for the proposed method, as compared with the other existing methods.

PLBP: An effective local binary patterns texture descriptor with pyramid representation

Towards consensus on managing high mammographic density in population breast screening?