Local ZigZag Max histograms of pooling pattern for texture classification. Issue 7 (1st April 2019)

• Record Type: Journal Article
• Authors: Roy, Swalpa Kumar; Dubey, Shiv Ram; Chaudhuri, Bidyut B.
• Is Part Of: Electronics letters. Volume 55:Issue 7(2019)
• Page Start: 382
• Page End: 384
• Publication Date: 2019-04-01
• Subjects: computer vision -- image classification -- image texture; CUReT texture databases -- LZMHPP descriptor -- image descriptor -- computer vision -- image patch -- local zigzag max histograms of pooling pattern -- texture classification model -- zigzag ordering mechanism -- KTH‐TIPS texture databases -- complementary zigzag weighted structures; Electronics -- Periodicals; 621.381
• DOI: 10.1049/el.2018.7631
• Languages: English
• ISSNs: 0013-5194

Abstract

Abstract : The efficiency of any texture classification model confides on descriptor used for similarity matching. The formation of image descriptor is a challenging and important task in computer vision. This Letter introduces a local ZigZag Max histograms of pooling pattern (LZMHPP) for classification of texture images. To compute the descriptor, first the dissimilarity between centre pixel and its neighbours is computed for each image patch over the whole image, then the dissimilarity map is encoded with different type of ZigZag ordering mechanism, and finally the Max histograms pooling is used to form LZMHPP descriptor from two complementary ZigZag weighted structures and achieves sufficient robustness under geometric variations. The experimental study on KTH‐TIPS and CUReT texture databases indicates the efficiency and supremacy of LZMHPP descriptor for texture classification.