Adaptive local binary pattern with oriented standard deviation (ALBPS) for texture classification

Abstract

A new method to describe texture images using a hybrid combination of local and global texture descriptors is proposed in this paper. In this regard, a new adaptive local binary pattern (ALBP) descriptor is presented in order to carry out the local description. It is built by adding oriented standard deviation information to an ALBP descriptor in order to achieve a more complete representation of the images, and hence, it has been called adaptive local binary pattern with oriented standard deviation (ALBPS). Regarding semen vitality assessment, ALBPS outperformed previous literature works with an 81.88% accuracy and also yielded higher hit rates than the LBP and ALBP baseline methods. Concerning the global description of the images, several classical texture algorithms were tested and a descriptor based on wavelet transform and Haralick feature extraction (wavelet concurrent feature 13 (WCF13)) obtained the best results. Both local and global descriptors were combined, and the classification was carried out with a support vector machine. Two data sets have been evaluated: textures under varying illumination, pose and scale (KTH-TIPS) 2a data set and a second spermatozoa boar data set used to distinguish between dead or alive sperm heads. Therefore, our proposal is novel in three ways. First, a new local feature extraction method ALBPS is introduced. Second, a hybrid method combining the proposed local ALBPS and a global descriptor is presented, outperforming our first approach and all other methods evaluated for this problem. Third, texture classification accuracy is greatly improved with the two former texture descriptors presented. F score and accuracy values were computed in order to measure the performance. The best overall result was yielded by combining ALBPS with WCF13, reaching an F score = 0.886 and an accuracy of 85.63% in the spermatozoa data set and an 84.45% of hit rate in the KTH-TIPS 2a.

Lung Cancer Prediction Using Neural Network Ensemble with Histogram of Oriented Gradient Genomic Features

“…LBP is a nonparametric method developed by Ojala et al [ 27 ] for the extraction of local spatial features from images. The theoretical definition of the basic LBP [ 27 , 29 ] is very simple, which forms the basis of its reputation as a computationally efficient image texture descriptor in the image processing research domain [ 65 , 66 ]. The MATLAB R2012a implementation of LBP algorithm was applied to the encoded genomic dataset in this study to obtain LBP features for the normal and mutated genomic samples.…”

“…The HOG feature descriptor is nominated for this study because it adequately captures the local appearance and shape of an object [ 28 ]. On the other hand, the LBP was considered for experimentation because of its capability to properly describe the texture of an image [ 27 , 29 ]. These core characteristics of HOG and LBP are paramount for detecting and discriminating the varying shapes and textures of the Voss-mapped genomic features in this study.…”

Textile Retrieval Based on Image Content from CDC and Webcam Cameras in Indoor Environments

“…Guo et al developed several modifications to LBP such as LBP variance (LBPV) [ 47 ], complete LBP (CLBP) [ 48 ] or adaptive LBP (ALBP) [ 49 ]. García-Olalla et al introduced algorithms to enhance LBP description [ 50 , 51 , 52 ], developing a new booster method that can be fused with LBP in order to improve accuracy results [ 53 ]. We refer the reader to [ 54 , 55 ] for a general framework and a taxonomy of local binary patterns variants.…”

Acrosome integrity assessment of boar spermatozoa images using an early fusion of texture and contour descriptors

“…Most of the works are also based on texture description. Sánchez et al [24] used the intensity distribution of the cytoplasm densities of the cells whereas [25] adds standard deviation information to the Local Binary Pattern (LBP) descriptor, [26] presents a new textural descriptor called NCSR and [27] shows the performance of LTP texture descriptor.…”