Structural difference histogram representation for texture image classification. Issue 2 (1st February 2017)

• Record Type: Journal Article
• Authors: Feng, Jinwang; Liu, Xinliang; Dong, Yongsheng; Liang, Lingfei; Pu, Jiexin
• Is Part Of: IET image processing. Volume 11:Issue 2(2017)
• Page Start: 118
• Page End: 125
• Publication Date: 2017-02-01
• Subjects: image texture -- image classification -- image representation -- image fusion -- image segmentation -- feature extraction; structural difference histogram representation -- texture image classification -- local binary pattern -- texture descriptor -- neighbour difference information -- segmented structure pattern fusion -- SSP fusion -- refined LBP -- RLBP -- NDP -- image texture, global contour information -- SSP histogram feature -- local texture information represention -- texture image local patch -- Columbia‐Utrecht reflectance database -- Brodatz database; Image processing -- Periodicals; 621.36705
• DOI: 10.1049/iet-ipr.2016.0495
• Languages: English
• ISSNs: 1751-9659

Abstract

Abstract : Local binary pattern (LBP) is a frequently‐used texture descriptor. Lots of LBP‐variants have been proposed to improve its performance of representing textures. However, most of them ignore the global and neighbour‐difference information of an image texture. In this study, the authors propose a structural difference histogram representation by fusing the segmented structure pattern (SSP), the refined LBP (RLBP) and the neighbour‐difference pattern (NDP) for texture classification. Particularly, the segmented structure, which contains the global contour information of an image texture, is first constructed to compute its SSP histogram feature. Simultaneously, the RLBP is defined to represent the local texture information. Furthermore, the NDP is presented to describe differences between neighbours of a centre pixel in the local patch of texture images. Experimental results on Brodatz and Columbia‐Utrecht reflectance databases indicate that the proposed method can achieve the satisfactory classification accuracy compared with several representative methods.