Standard Local Binary Pattern Research Articles

A Proposed Framework for Face Recognition using Enhanced Local Binary Pattern Algorithm with Chinese Remainder Theorem

One of the biometric methods that have recently gained attention across the globe is Face Recognition. This was due to the availability of practicable technologies, including movable results. Several studies have been carried out on face recognition for decades, but the problem is still largely unsolved. Significant progress has been made recently in this area as a result of advancements in face modeling and analysis techniques. While a system has been developed for face recognition, the problem of high processing time is still largely unresolved. This research framework proposed an Enhanced Local Binary Pattern (ELBP) algorithm for face recognition. The Local Binary Pattern (LBP) algorithm is a method used in facial feature dimensionality reduction. Standard LBP had challenges of computational complexity. Therefore, the LBP will be enhanced with the Chinese Remainder Theorem (CRT) and will be used for feature extraction to reduce computational time, Chicken Swarm Optimization (CSO) will be used for feature selection and classification will be done using a Support Vector Machine (SVM). Performance Evaluation of the system will be done by comparing the computation time result obtained from the combination of LBP-CSO and ELBP-CSO. The ELBP-CSO is expected to have a lower computation recognition time than the LBP-CSO.

Artificial Bee Colony Programming Descriptor for Multi-Class Texture Classification

Texture classification is one of the machine learning methods that attempts to classify textures by evaluating samples. Extracting related features from the samples is necessary to successfully classify textures. It is a very difficult task to extract successful models in the texture classification problem. The Artificial Bee Colony (ABC) algorithm is one of the most popular evolutionary algorithms inspired by the search behavior of honey bees. Artificial Bee Colony Programming (ABCP) is a recently introduced high-level automatic programming method for a Symbolic Regression (SR) problem based on the ABC algorithm. ABCP has applied in several fields to solve different problems up to date. In this paper, the Artificial Bee Colony Programming Descriptor (ABCP-Descriptor) is proposed to classify multi-class textures. The models of the descriptor are obtained with windows sliding on the textures. Each sample in the texture dataset is defined instance. For the classification of each texture, only two random selected instances are used in the training phase. The performance of the descriptor is compared standard Local Binary Pattern (LBP) and Genetic Programming-Descriptor (GP-descriptor) in two commonly used texture datasets. When the results are evaluated, the proposed method is found to be a useful method in image processing and has good performance compared to LBP and GP-descriptor.

Epileptic EEG Identification via LBP Operators on Wavelet Coefficients.

The automatic identification of epileptic electroencephalogram (EEG) signals can give assistance to doctors in diagnosis of epilepsy, and provide the higher security and quality of life for people with epilepsy. Feature extraction of EEG signals determines the performance of the whole recognition system. In this paper, a novel method using the local binary pattern (LBP) based on the wavelet transform (WT) is proposed to characterize the behavior of EEG activities. First, the WT is employed for time-frequency decomposition of EEG signals. After that, the "uniform" LBP operator is carried out on the wavelet-based time-frequency representation. And the generated histogram is regarded as EEG feature vector for the quantification of the textural information of its wavelet coefficients. The LBP features coupled with the support vector machine (SVM) classifier can yield the satisfactory recognition accuracies of 98.88% for interictal and ictal EEG classification and 98.92% for normal, interictal and ictal EEG classification on the publicly available EEG dataset. Moreover, the numerical results on another large size EEG dataset demonstrate that the proposed method can also effectively detect seizure events from multi-channel raw EEG data. Compared with the standard LBP, the "uniform" LBP can obtain the much shorter histogram which greatly reduces the computational burden of classification and enables it to detect ictal EEG signals in real time.

3D local ternary co-occurrence patterns for natural, texture, face and bio medical image retrieval

Abstract In this paper, a novel feature called three dimensional local ternary co-occurrence pattern (3D-LTCoP) is proposed for natural, texture, face and biomedical image retrieval. Standard local binary pattern and its variants like local ternary patterns, local derivative patterns, local tetra patterns etc. encode relationship between reference pixel and neighboring pixels in a two dimensional plane of the image. The edge distribution information in these local patterns are extracted using first-order derivatives and are represented in the form of histogram. Proposed technique of feature representation draws a three dimensional cubical image block in the local region using Gaussian filtered images and extracts relationship between reference pixel and neighboring pixels in five diverse directions of the 3D block. Further, frequency analysis of ternary patterns is performed by storing mutual local directional information in the co-occurrence matrix. Experiments are conducted on six benchmark databases ranging from natural, texture, face to biomedical categories to observe the robustness of the proposed feature. Results are analyzed and compared with typical state-of-the-art local patterns and superiority of the proposed technique is clearly evident in terms of performance evaluation measures.

Multi-dimensional multi-directional mask maximum edge pattern for bio-medical image retrieval

Authors have proposed novel multi-dimensional multi-directional mask maximum edge patterns for the bio-medical image retrieval. Standard local binary patterns encode relationship of neighbor pixels with center pixel. Local mesh patterns encode the relationship between adjacent pixels surrounding the center pixel. Proposed approach encodes relationship of neighbour pixels in adjacent planes of a multi-dimensional image, in three stages. In the first stage, five sub images are formed by traversing in five different directions on three planes of a multi-dimensional image. In the second stage, directional masks are applied on each sub image to find directional edges. In stage three, maximum edge patterns are found based on the directions of the directional edges. To examine performance analysis of the proposed algorithm, we tested proposed algorithm on three benchmark databases, which gives retrieval accuracy $$56.93\%$$ for top 5 images, 93.36 and $$62.49\%$$ for top 10 images on MESSIDOR (Retinal images), VIA/I-ELCAP (CT images) and OASIS-MRI databases respectively in terms of average retrieval precision. The comparison reflects, there is considerable improvement in the performance.

New Steganalytic Features for Spatial Image Steganography Based on Derivative Filters and Threshold LBP Operator

The standard local binary pattern (LBP) operator shows its versatility in performing image classification-related tasks, including texture analysis, object recognition, and steganalysis. However, a conventional well-designed scheme utilizing LBP operator and histogram-based features does not have obvious advantage when compared with the well-known steganalytic scheme spatial rich model (SRM). In this paper, we propose an adapted LBP version, called threshold LBP (TLBP), to reveal the artifacts caused by data embedding. In the proposed steganalytic scheme, the TLBP operation is performed on residual images which are obtained by using a set of high-order derivative filters to capture intricate relationships among pixels. After performing TLBP operation, second order co-occurrence matrix features are formed and then processed with aggregation and non-linear mapping for boosting feature effectiveness. Experimental results show that the proposed TLBP features prevail over SRM features under various steganographic conditions.

Local mesh ternary patterns: a new descriptor for MRI and CT biomedical image indexing and retrieval

This paper proposes a new pattern-based feature called local mesh ternary pattern for biomedical image indexing and retrieval. The standard local binary patterns (LBP) and local ternary patterns (LTP) encode the greyscale relationship between the centre pixel and its surrounding neighbours in two-dimensional (2D) local region of an image, whereas the proposed method encodes the greyscale relationship among the neighbours for a given centre pixel with three selected directions of mesh patterns which is generated from 2D image. The novelty of the proposed method is that it uses ternary patterns from mesh patterns of an image to encode more spatial structure information which leads to better retrieval. The experiments have been carried out for proving the worth of proposed algorithm on three different types of benchmarked biomedical databases; (i) computed tomography (CT) scanned lung image databases named as LIDC-IDRI-CT and VIA/I–ELCAP-CT, (ii) brain magnetic resonance imaging (MRI) database named as OASIS-MRI. The results demonstrate that the proposed method yields better performance in terms of average retrieval precision and average retrieval rate over state-of-the-art feature extraction techniques like LBP, LTP, local mesh pattern, etc.

Directional local ternary quantized extrema pattern: A new descriptor for biomedical image indexing and retrieval

This paper proposes a new pattern descriptor called directional local ternary quantized extrema pattern (DLTerQEP) for biomedical image indexing and retrieval. The standard local binary patterns (LBPs) and local ternary patterns (LTPs) encode the gray scale relationship between the center pixel and its surrounding neighbors in two dimensional (2D) local region of an image whereas the proposed method encodes the spatial relation between any pair of neighbors in a local region along the given directions (i.e., 0°, 45°, 90° and 135°) for a given center pixel in an image. The novelty of the proposed method is it uses ternary patterns from horizontal–vertical–diagonal–antidiagonal (HVDA7) structure of directional local extrema values of an image to encode more spatial structure information which lead to better retrieval. DLTerQEP also provides a significant increase in discriminative power by allowing larger local pattern neighborhoods. The experiments have been carried out for proving the worth of proposed algorithm on three different types of benchmark biomedical databases; (i) computed tomography (CT) scanned lung image databases named as LIDC-IDRI-CT and VIA/I-ELCAP-CT, (ii) brain magnetic resonance imaging (MRI) database named as OASIS-MRI. The results demonstrate the superiority of the proposed method in terms of average retrieval precision (ARP) and average retrieval rate (ARR) over state-of-the-art feature extraction techniques like LBP, LTP and LQEP etc.

Medical image classification using spatial adjacent histogram based on adaptive local binary patterns

Medical image recognition is an important task in both computer vision and computational biology. In the field of medical image classification, representing an image based on local binary patterns (LBP) descriptor has become popular. However, most existing LBP-based methods encode the binary patterns in a fixed neighborhood radius and ignore the spatial relationships among local patterns. The ignoring of the spatial relationships in the LBP will cause a poor performance in the process of capturing discriminative features for complex samples, such as medical images obtained by microscope. To address this problem, in this paper we propose a novel method to improve local binary patterns by assigning an adaptive neighborhood radius for each pixel. Based on these adaptive local binary patterns, we further propose a spatial adjacent histogram strategy to encode the micro-structures for image representation. An extensive set of evaluations are performed on four medical datasets which show that the proposed method significantly improves standard LBP and compares favorably with several other prevailing approaches.

Biomedical Image Indexing and Retrieval Descriptors: A Comparative Study

This paper focuses on the comparison of two new proposed pattern descriptors i.e., local mesh ternary pattern (LMeTerP) and directional local ternary quantized extrema pattern (DLTerQEP) for biomedical image indexing and retrieval. The standard local binary patterns (LBP) and local ternary patterns (LTP) encode the gray scale relationship between the center pixel and its surrounding neighbors in two dimensional (2D) local region of an image whereas the former descriptor encodes the gray scale relationship among the neighbors for a given center pixel with three selected directions of mess patterns which is generated from 2D image and later descriptor encodes the spatial relation between any pair of neighbors in a local region along the given directions (i.e., 0̊, 45̊, 90̊ and 135̊) for a given center pixel in an image. The novelty of the proposed descriptors is that they use ternary patterns from images to encode more spatial structure information which lead to better retrieval. The experimental results demonstrate the superiority of the new techniques in terms of average retrieval precision (ARP) and average retrieval rate (ARR) over state-of-the-art feature extraction techniques (like LBP, LTP, LQEP, LMeP etc.) on three different types of benchmark biomedical databases.

Local Gabor maximum edge position octal patterns for image retrieval

In this paper, a new coding scheme, local Gabor maximum edge position octal patterns (LGMEPOP) is proposed for content based image retrieval. The standard local binary pattern (LBP) collects the sign edge (binary code) information between the center pixel and its surrounding neighbors in an image. Further, the concept of LBP is extended to local maximum edge binary pattern (LMEBP) which collects the sign code (binary code) using the magnitude edges. These magnitude edges are collected based on the maximum edges between the center pixel and its surrounding neighbors in an image. In this paper, we propose a new feature descriptor, octal code which is coded based on the maximum edge positions (MEP) on Gabor responses. Specially, each pixel of every Gabor response gains eight edges based on the relationship between the referenced pixel and its neighbors. LGMEPOP utilizes the first three dominant (maximum) edge positions in an octal code generation. Then, these three maximum edge positions are encoded into three-eight octal numbers to produce the LGMEPOP. Further, the LGMEPOP is classified into two categories which are named as sign maximum edge position octal pattern (SMEPOP) and magnitude maximum edge position octal pattern (MMEPOP). The SMEPOP and MMEPOP are coded based on the sign and magnitudes of dominant edges respectively. The performance of the proposed method is tested on two benchmark databases. The results after being investigated show a significant improvement as compared to other existing methods (LBP and LBP variants) in terms of average retrieval precision (ARP) and average retrieval rate (ARR) on Corel-5000 and Corel-10000 databases.

Directional local ternary patterns for multimedia image indexing and retrieval

In this paper, we proposed a novel multimedia image indexing and retrieval algorithm using Directional Local Ternary Pattern (DLTP) for Content-Based Image Retrieval (CBIR). The standard Local Binary Pattern (LBP) and Local Ternary Pattern (LTP) encode the relationship between reference pixel and its surrounding neighbours by computing grey-level difference. The proposed method extracts the directional edge information based on local ternary value (−1, 0, 1) instead of binary value (0, 1) in 0°, 45°, 90°, and 135° directions of an image. The performance of the proposed method is compared with local binary pattern LBP, Block-based LBP (BLK-LBP), Centre-Symmetric LBP (CS-LBP), Local Edge Patterns for Segmentation (LEPSEG), Local Edge Patterns for Image Retrieval (LEPINV) and other existing transform domain methods based on Corel-5000 (DB1) and Corel-10000 (DB2) databases. The retrieval performance of DLTP shows better improvement in terms of their performance measures as compared with the standard LBP.

RADIUS BASED BLOCK LOCAL BINARY PATTERN ON T-ZONE FACE AREA FOR FACE RECOGNITION

This study presents a comparison of recognition performance between feature extraction on the T-Zone face area and Radius based block on the critical point. A T-Zone face image is first divided into small regions where Local Binary Pattern (LBP) histograms are extracted and then concatenated into a single feature vector. This feature vector will further reduce the dimensionality scope by using the well established Principle Component Analysis (PCA) technique. On the other hand, while the original LBP techniques focus in dividing the whole image into certain regions, we proposed a new scheme, which focuses on critical region, which gives more impact to the recognition performance. This technique is known as Radius Based Block Local Binary Pattern (RBB-LBP). Here we focus on three main area which is eye (including eyebrow), mouth and nose. We defined four critical point represent left eye, right eye, nose and mouth, from this four main point we derived the next nine point. This approach will automatically create the redundancy in various regions and for every radius size window a robust histogram with all possible labels constructed. Experiments have been carried out on the different sets of the Olivetti Research Laboratory (ORL) database. RBB-LBP obtained high recognition rates when compared to standard LBP, LBP+PCA and also on T-Zone area. Our result shows of 16% improvement compared with LBP+PCA and 6% improvement compared with LBP. Our studies proves that the RBB-LBP method, reduce the length of the feature vector, while the recognition performance is improved.

English

Content Based Image Retrieval(CBIR) is one of the prominent area to retrieve images from a large collection of database. There is wide range of texture analysis techniques used for feature extraction of an image. In this paper, we have proposed image indexing and retrieval algorithm for texture extraction using Local tetra pattern (LTrP). The local binary pattern (LBP) and local ternary pattern (LTP) encode the texture features of an image depending on the grey level difference between reference pixel and its neighbors. The LTrP encodes the relationship between the reference pixel and its neighbors by using the first-order derivatives in vertical and horizontal directions. Local tetra pattern (LTrP) extracts information based on the distribution of edges which are coded using four directions. To get the retrieval result we used Corel 1000 database. The performance of the proposed method is measured in terms of average precision and average recall. The performance analysis shows that the proposed method improves the retrieval result as compared with standard LBP.

Color Directional Local Quinary Patterns for Content Based Indexing and Retrieval

AbstractThis paper presents a novel evaluationary approach to extract color-texture features for image retrieval application namely Color Directional Local Quinary Pattern (CDLQP). The proposed descriptor extracts the individual R, G and B channel wise directional edge information between reference pixel and its surrounding neighborhoods by computing its grey-level difference based on quinary value (−2, −1, 0, 1, 2) instead of binary and ternary value in 0°, 45°, 90°, and 135° directions of an image which are not present in literature (LBP, LTP, CS-LBP, LTrPs, DExPs, etc.). To evaluate the retrieval performance of the proposed descriptor, two experiments have been conducted on Core-5000 and MIT-Color databases respectively. The retrieval performances of the proposed descriptor show a significant improvement as compared with standard local binary pattern LBP, center-symmetric local binary pattern (CS-LBP), Directional binary pattern (DBC) and other existing transform domain techniques in IR system.

Support Local Pattern and its Application to Disparity Improvement and Texture Classification

The local binary pattern (LBP) and its variants have been widely investigated in many image processing and computer vision applications due to their robust ability to capture local image structures and their computational simplicity. The existing LBPs extract local structure information by establishing a relationship between the central pixel and its adjacent pixels. However, most LBPs miss the relationship among all of the pixels in the local region. Therefore, this paper proposes a novel model to establish this relationship by introducing a support LBP. The proposed model improves the performance of the existing LBP methods and results in lower sensitivity to illumination changes and radiometric variations. Moreover, the proposed model has been successfully investigated in two applications: disparity map generation and texture classification. For disparity map generation, the proposed model reduces the root mean square (RMS) error by 23.6% (in Baby1 dataset, Middlebury), and 16.58% (in Aloe dataset, Middlebury) as compared with the standard LBP under radiometric variation conditions. Moreover, the proposed model reduces the RMS by 28.11% as compared with the standard LBP under the Gaussian noise condition in the ESATS dataset. For texture classification applications, the proposed model improves the classification results from 96.26% to 98.13% on the Outext database, from 88.03% to 91.41% on the Xu database, and from 94.00% to 96.67% on the KTH-TIPS database as compared with the completed LBP.

Many local pattern texture features: which is better for image-based multilabel human protein subcellular localization classification?

Human protein subcellular location prediction can provide critical knowledge for understanding a protein's function. Since significant progress has been made on digital microscopy, automated image-based protein subcellular location classification is urgently needed. In this paper, we aim to investigate more representative image features that can be effectively used for dealing with the multilabel subcellular image samples. We prepared a large multilabel immunohistochemistry (IHC) image benchmark from the Human Protein Atlas database and tested the performance of different local texture features, including completed local binary pattern, local tetra pattern, and the standard local binary pattern feature. According to our experimental results from binary relevance multilabel machine learning models, the completed local binary pattern, and local tetra pattern are more discriminative for describing IHC images when compared to the traditional local binary pattern descriptor. The combination of these two novel local pattern features and the conventional global texture features is also studied. The enhanced performance of final binary relevance classification model trained on the combined feature space demonstrates that different features are complementary to each other and thus capable of improving the accuracy of classification.

Local Mesh Patterns Versus Local Binary Patterns: Biomedical Image Indexing and Retrieval

In this paper, a new image indexing and retrieval algorithm using local mesh patterns are proposed for biomedical image retrieval application. The standard local binary pattern encodes the relationship between the referenced pixel and its surrounding neighbors, whereas the proposed method encodes the relationship among the surrounding neighbors for a given referenced pixel in an image. The possible relationships among the surrounding neighbors are depending on the number of neighbors, P. In addition, the effectiveness of our algorithm is confirmed by combining it with the Gabor transform. To prove the effectiveness of our algorithm, three experiments have been carried out on three different biomedical image databases. Out of which two are meant for computer tomography (CT) and one for magnetic resonance (MR) image retrieval. It is further mentioned that the database considered for three experiments are OASIS-MRI database, NEMA-CT database, and VIA/I-ELCAP database which includes region of interest CT images. The results after being investigated show a significant improvement in terms of their evaluation measures as compared to LBP, LBP with Gabor transform, and other spatial and transform domain methods.

Local Tetra Patterns: A New Feature Descriptor for Content-Based Image Retrieval

In this paper, we propose a novel image indexing and retrieval algorithm using local tetra patterns (LTrPs) for content-based image retrieval (CBIR). The standard local binary pattern (LBP) and local ternary pattern (LTP) encode the relationship between the referenced pixel and its surrounding neighbors by computing gray-level difference. The proposed method encodes the relationship between the referenced pixel and its neighbors, based on the directions that are calculated using the first-order derivatives in vertical and horizontal directions. In addition, we propose a generic strategy to compute nth-order LTrP using (n - 1)th-order horizontal and vertical derivatives for efficient CBIR and analyze the effectiveness of our proposed algorithm by combining it with the Gabor transform. The performance of the proposed method is compared with the LBP, the local derivative patterns, and the LTP based on the results obtained using benchmark image databases viz., Corel 1000 database (DB1), Brodatz texture database (DB2), and MIT VisTex database (DB3). Performance analysis shows that the proposed method improves the retrieval result from 70.34%/44.9% to 75.9%/48.7% in terms of average precision/average recall on database DB1, and from 79.97% to 85.30% and 82.23% to 90.02% in terms of average retrieval rate on databases DB2 and DB3, respectively, as compared with the standard LBP.

Directional local extrema patterns: a new descriptor for content based image retrieval

In this paper, a new algorithm using directional local extrema patterns meant for content-based image retrieval application is proposed. The standard local binary pattern (LBP) encodes the relationship between reference pixel and its surrounding neighbors by comparing gray-level values. The proposed method differs from the existing LBP in a manner that it extracts the directional edge information based on local extrema in 0 $$^{\circ }$$ , 45 $$^{\circ }$$ , 90 $$^{\circ }$$ , and 135 $$^{\circ }$$ directions in an image. Performance is compared with LBP, block-based LBP (BLK_LBP), center-symmetric local binary pattern (CS-LBP), local edge patterns for segmentation (LEPSEG), local edge patterns for image retrieval (LEPINV), and other existing transform domain methods by conducting four experiments on benchmark databases viz. Corel (DB1) and Brodatz (DB2) databases. The results after being investigated show a significant improvement in terms of their evaluation measures as compared with other existing methods on respective databases.