Multi-resolution Local Binary Pattern Research Articles

Steel surface defect classification using multi-resolution empirical mode decomposition and LBP

In this work we introduce Multi-Resolution Empirical Mode Decomposition (MREMD) as an image decomposition method that simplifies the implementation of Empirical Mode Decomposition (EMD) for bidimensional data. The proposed method is used in conjunction with the local binary pattern (LBP) to classify the images of six types of defects that can be found on the surface of rolled steel. The process starts by performing MREMD on the training images to obtain the first bidimensional intrinsic mode function (BIMF). Then features are extracted from the images and their first BIMF using the LBP. These features are used to train an artificial neural network (ANN) classifier. After training, given an unknown test image containing a defect, MREMD is applied on it to obtain its first BIMF. Next, LBP features are extracted from the image and its first BIMF and these features are fed to the trained ANN classifier to assign the image to one of the six defect classes. The classification process is carried out on 900 test images of the NEU database of six types of surface defects. The approach achieves an overall accuracy that is better than the result obtained using the LBP features alone. The main contribution of this paper is the introduction of multi resolution envelope interpolation using downsampling and upsampling with a fixed window size that reduces the execution time and decrease the sensitivity of the resulting BIMFs to the positions and number of extrema in the input image.

Computer-aided classification of the mitral regurgitation using multiresolution local binary pattern

This paper introduces a computer-aided classification (CAC) system for the severity analysis of mitral regurgitation (MR) utilizing multiresolution local binary pattern variants texture features. Initially, the Gaussian pyramid has been used as a multiresolution technique. Subsequently, seven variants of the local binary pattern (LBP) have been employed to extract the features. At last, support vector machine and random forest classifiers are used for classification. The performances of conventional LBP variants and proposed features have been evaluated on MR image database in three classes, i.e., mild, moderate, and severe, in three different views. The Gaussian pyramid-based center-symmetric local binary pattern performed well in all three views. The achieved classification accuracies are 95.66 ± 0.98% in the apical 2 chamber, 94.47 ± 1.91% in the apical 4 chamber and 94.21 ± 1.31% in parasternal long axis views using SVM classifier with the tenfold cross-validation. The outcomes of paper confirm that the performance of the conventional LBP features is enhanced significantly and the proposed CAC system is useful in assisting cardiologists in the severity analysis of MR.

Feature fusion for lung nodule classification.

This article examines feature-based nodule description for the purpose of nodule classification in chest computed tomography scanning. Three features based on (i) Gabor filter, (ii) multi-resolution local binary pattern (LBP) texture features and (iii) signed distance fused with LBP which generates a combinational shape and texture feature are utilized to provide feature descriptors of malignant and benign nodules and non-nodule regions of interest. Support vector machines (SVMs) and k-nearest neighbor (kNN) classifiers in serial and two-tier cascade frameworks are optimized and analyzed for optimal classification results of nodules. A total of 1191 nodule and non-nodule samples from the Lung Image Data Consortium database is used for analysis. Classification using SVM and kNN classifiers is examined. The classification results from the two-tier cascade SVM using Gabor features showed overall better results for identifying non-nodules, malignant and benign nodules with average area under the receiver operating characteristics (AUC-ROC) curves of 0.99 and average f1-score of 0.975 over the two tiers. In the results, higher overall AUCs and f1-scores were obtained for the non-nodules cases using any of the three features, showing the greatest distinguishability over nodules (benign/malignant). SVM and kNN classifiers were used for benign, malignant and non-nodule classification, where Gabor proved to be the most effective of the features for classification. The cascaded framework showed the greatest distinguishability between benign and malignant nodules.

Multi-Resolution Local Binary Pattern for Assessing Cervical Ripening

Multi-Resolution Local Binary Pattern for Assessing Cervical Ripening

Multiresolution local binary pattern variants based texture feature extraction techniques for efficient classification of microscopic images of hardwood species

In this paper, multiresolution local binary pattern (MRLBP) variants based texture feature extraction techniques have been proposed to categorize hardwood species into its various classes. Initially, discrete wavelet transform (DWT) has been used to decompose each image up to 7 levels using Daubechies wavelet (db2) as decomposition filter. Subsequently, six texture feature extraction techniques (local binary pattern and its variants) are employed to obtain substantial features of these images at different levels. Three classifiers, namely, linear discriminant analysis (LDA), linear and radial basis function (RBF) kernel support vector machine (SVM), have been used to classify the images of hardwood species. Thereafter, classification results obtained from conventional and MRLBP variants based texture feature extraction techniques with different classifiers have been compared. For 10-fold cross validation approach, texture features acquired using discrete wavelet transform based uniform completed local binary pattern (DWTCLBPu2) feature extraction technique has produced best classification accuracy of 97.40±1.06% with linear SVM classifier. This classification accuracy has been achieved at the 3rd level of image decomposition using full feature (1416) dataset. Further, reduction in dimension of texture features (325 features) by principal component analysis (PCA) has been done and the best classification accuracy of 97.87±0.82% for DWTCLBPu2 at the 3rd level of image decomposition has been obtained using LDA classifier. The DWTCLBPu2 texture features have also established superiority among the MRLBP techniques with reduced dimension features for randomly divided database into fix training and testing ratios.

Efficient data mining for local binary pattern in texture image analysis

Local binary pattern (LBP) is a simple gray scale descriptor to characterize the local distribution of the gray levels in an image. Multi-resolution LBP and/or combinations of the LBPs have shown to be effective in texture image analysis. However, it is unclear what resolutions or combinations to choose for texture analysis. Examining all the possible cases is impractical and intractable due to the exponential growth in a feature space. This limits the accuracy and time- and space-efficiency of LBP. Here, we propose a data mining approach for LBP, which efficiently explores a high-dimensional feature space and finds a relatively smaller number of discriminative features. The features can be any combinations of LBPs. These may not be achievable with conventional approaches. Hence, our approach not only fully utilizes the capability of LBP but also maintains the low computational complexity. We incorporated three different descriptors (LBP, local contrast measure, and local directional derivative measure) with three spatial resolutions and evaluated our approach using two comprehensive texture databases. The results demonstrated the effectiveness and robustness of our approach to different experimental designs and texture images.

Facial action unit recognition using multi-class classification

Within the context of facial expression classification using the facial action coding system (FACS), we address the problem of detecting facial action units (AUs). Feature extraction is performed by generating a large number of multi-resolution local binary pattern (MLBP) features and then selecting from these using fast correlation-based filtering (FCBF). The need for a classifier per AU is avoided by training a single error-correcting output code (ECOC) multi-class classifier to generate occurrence scores for each of several AU groups. A novel weighted decoding scheme is proposed with the weights computed using first order Walsh coefficients. Platt scaling is used to calibrate the ECOC scores to probabilities and appropriate sums are taken to obtain separate probability estimates for each AU individually. The bias and variance properties of the classifier are measured and we show that both these sources of error can be reduced by enhancing ECOC through bootstrapping and weighted decoding.

Multiresolution local binary pattern texture analysis combined with variable selection for application to false-positive reduction in computer-aided detection of breast masses on mammograms

In this paper, a new and novel approach is designed for extracting local binary pattern (LBP) texture features from the computer-identified mass regions, aiming to reduce false-positive (FP) detection in a computerized mass detection framework. The proposed texture feature, the so-called multiresolution LBP feature, is well able to characterize the regional texture patterns of core and margin regions of a mass, as well as to preserve the spatial structure information of the mass. In addition, to maximize a complementary effect on improving classification accuracy, multiresolution texture analysis has been incorporated into the extraction of LBP features. Further, SVM-RFE-based variable selection strategy is applied for selecting an optimal subset of variables of multiresolution LBP texture features to maximize the separation between breast masses and normal tissues. Extensive and comparative experiments have been conducted to evaluate the proposed method on two public benchmark mammogram databases (DBs). Experimental results show that the proposed multiresolution LBP features (extracted from automatically segmented mass boundaries) outperform other state-of-the-art texture features developed for FP reduction. Our results also indicate that combining our multiresolution LBP features with variable selection strategy is an effective solution for reducing FP signals in computer-aided detection (CAD) of mammographic masses.

Multiresolution LBP Correlogram for Texture Image Indexing and Retrieval

A new image indexing and retrieval algorithm known as local binary pattern (LBP) correlogram is presented in this paper. LBP histogram captures only the patterns distribution in a texture while the spatial correlation between the pair of patterns is gathered by LBP correlogram. Multi-resolution texture decomposition and color correlation has been efficiently used in the proposed method where multi-resolution texture images are computed using Gaussian filter for collection of LBPs from these particular textures. Eventually, feature vectors are constructed by making into play the auto-correlation that exists between binary patterns. The retrieval results of the proposed method are examined on different texture image databases viz Brodatz database (DB1), MIT VisTex database (DB2), rotated Brodatz database (DB3) and small set of rotated Brodatz database (DB4), and shows a major improvement in terms of average retrieval rate as when weighed against with LBP histogram and some existing transform domain technique.

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

Local binary pattern (LBP) is an effective texture descriptor which has successful applications in texture classification and face recognition. Many extensions are made for conventional LBP descriptors. One of the extensions is dominant local binary patterns which aim at extracting the dominant local structures in texture images. The second extension is representing LBP descriptors in Gabor transform domain (LGBP). The third extension is multi-resolution LBP (MLBP). Another extension is dynamic LBP for video texture extraction. In this paper, we extend the conventional local binary pattern to pyramid transform domain (PLBP). By cascading the LBP information of hierarchical spatial pyramids, PLBP descriptors take texture resolution variations into account. PLBP descriptors show their effectiveness for texture representation. Comprehensive comparisons are made for LBP, MLBP, LGBP, and PLBP. Performances of no sampling, partial sampling and spatial pyramid sampling approaches for the construction of PLBP texture descriptors are compared. The influences of pyramid generation approaches, and pyramid levels to PLBP based image categorization performances are discussed. Compared to the existing multi-resolution LBP descriptors, PLBP is with satisfactory performances and with low computational costs.

MULTI-VIEW GENDER CLASSIFICATION USING MULTI-RESOLUTION LOCAL BINARY PATTERNS AND SUPPORT VECTOR MACHINES

In this paper, we present a novel method for multi-view gender classification considering both shape and texture information to represent facial images. The face area is divided into small regions from which local binary pattern (LBP) histograms are extracted and concatenated into a single vector efficiently representing a facial image. Following the idea of local binary pattern, we propose a new feature extraction approach called multi-resolution LBP, which can retain both fine and coarse local micro-patterns and spatial information of facial images. The classification tasks in this work are performed by support vector machines (SVMs). The experiments clearly show the superiority of the proposed method over both support gray faces and support Gabor faces on the CAS-PEAL face database. A higher correct classification rate of 96.56% and a higher cross validation average accuracy of 95.78% have been obtained. In addition, the simplicity of the proposed method leads to very fast feature extraction, and the regional histograms and fine-to-coarse description of facial images allow for multi-view gender classification.

View-based recognition of real-world textures

A new method for recognizing 3D textured surfaces is proposed. Textures are modeled with multiple histograms of micro-textons, instead of more macroscopic textons used in earlier studies. The micro-textons are extracted with the recently proposed multiresolution local binary pattern operator. Our approach has many advantages compared to the earlier approaches and provides the leading performance in the classification of Columbia–Utrecht database textures imaged under different viewpoints and illumination directions. It also provides very promising results in the classification of outdoor scene images. An approach for learning appearance models for view-based texture recognition using self-organization of feature distributions is also proposed. The method performs well in experiments. It can be used for quickly selecting model histograms and rejecting outliers, thus providing an efficient tool for vision system training even when the feature data has a large variability.

Query Expansion by Text and Image Features in Image Retrieval

We present a two-pass image retrieval system in which retrieval techniques for text and image documents are combined in a novel approach. In the first pass, the text-based initial query is matched against the text captions of the images in the database to obtain the initial retrieved set. In the second pass, text and image features obtained from this initial retrieved set are used to expand the initial query. Additional images from the database are then retrieved based on the expanded query. The image features that we have used are color histograms, DC coefficients from the discrete cosine transform, and two texture features: multiresolution simultaneous autoregressive model and local binary pattern. These are low-level statistical image features that can be easily computed. Extensive experiments have been performed on 1019 color pictures of mixed variety with captions, relevance judgments and queries supplied by a national archives agency. Objective precision-recall results have been obtained with various combinations of text and image features. The results show that the image features do not perform well when used on their own. However, when image features are used in query expansion, they increase the average precision more significantly than text annotations. Moreover, these findings are valid at all precision levels and are not sensitive to the image feature acquisition parameters.