Attractive-and-repulsive center-symmetric local binary patterns for texture classification. (February 2019)

• Record Type: Journal Article
• Authors: El merabet, Y.; Ruichek, Y.; El idrissi, A.
• Is Part Of: Engineering applications of artificial intelligence. Volume 78(2019)
• Page Start: 158
• Page End: 172
• Publication Date: 2019-02
• Subjects: LBP -- CS-LBP -- ACS-LBP -- RCS-LBP -- ARCS-LBP -- Feature extraction -- Texture classification; Engineering -- Data processing -- Periodicals; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Ingénierie -- Informatique -- Périodiques; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; Artificial intelligence; Engineering -- Data processing; Expert systems (Computer science); Periodicals; 620.00285
• DOI: 10.1016/j.engappai.2018.11.011
• Languages: English
• ISSNs: 0952-1976

Abstract

Abstract: Aiming at the defect of Local Binary Pattern (LBP) and its variants, this paper presents a new modeling of the conventional LBP operator for texture classification. Named Attractive-and-Repulsive Center-Symmetric Local Binary Patterns ( ACS-LBP and RCS-LBP ), the proposed new texture descriptors preserve the advantageous characteristics of uniform LBP. Based on local attractive-and-repulsive characteristics, the proposed local texture modeling can really inherit good properties from both gradient and texture operators than the Center-Symmetric Local Binary Patterns (CS-LBP) does. Different from CS-LBP, which considers four doublets around the center pixel, the proposed methods take into account the four triplets corresponding to the vertical and horizontal directions, and the two diagonal directions by including the value of the central pixel in the modeling. In addition, Average Local Gray Level ( ALGL ), Average Global Gray Level ( AGGL ) and the median value over 3 × 3 neighborhood are introduced to capture both microstructure and macrostructure texture information. To capture the coarse and fine information of the features and thus to make ACS-LBP and RCS-LBP more robust and stable, multiscale ARCS-LBP descriptor is proposed. There is no necessity to learn texton dictionary, as in methods based on clustering, and no tuning of parameters is required to deal with different datasets. Extensive experiments performed on thirteen challenging representative texture databases show that the proposed operators can achieve impressive classification accuracy. Furthermore, we clearly validate the feasibility of the proposed ACS-LBP, RCS-LBP and ARCS-LBP descriptors by comparing their results with those obtained with a large number of recent state-of-the-art texture descriptors including deep features. Statistical significance of achieved accuracy improvement is demonstrated through Wilcoxon signed rank test. Highlights: We introduce a formal definition of Attractive-and-Repulsive Binary Thresholding Functions. Two new powerful descriptors: Attractive and Repulsive Center-Symmetric Local Binary Patterns are proposed. Multi-scale feature is incorporated by concatenating ACS-LBP and RCS-LBP into a single vector feature. Extensive evaluation on 13 challenging representative texture databases is performed. 72 recent state-of-the-art LBP variants as well as 3 deep learning features are evaluated.