MONOGENIC-LBP: A NEW APPROACH FOR ROTATION INVARIANT TEXTURE CLASSIFICATION

被引：46

作者：

Zhang, Lin ^{[1
]}

Zhang, Lei ^{[1
]}

Guo, Zhenhua ^{[1
]}

Zhang, David ^{[1
]}

机构：

[1] Hong Kong Polytech Univ, Dept Comp, Biometr Res Ctr, Hong Kong, Hong Kong, Peoples R China

来源：

2010 IEEE INTERNATIONAL CONFERENCE ON IMAGE PROCESSING | 2010年

关键词：

Texture classification; monogenic signal; LBP; IMAGE CLASSIFICATION; FEATURES;

D O I：

10.1109/ICIP.2010.5651885

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Analysis of two-dimensional textures has many potential applications in computer vision. In this paper, we investigate the problem of rotation invariant texture classification, and propose a novel texture feature extractor, namely Monogenic-LBP (M-LBP). M-LBP integrates the traditional Local Binary Pattern (LBP) operator with the other two rotation invariant measures: the local phase and the local surface type computed by the 1(st)-order and 2(nd)-order Riesz transforms, respectively. The classification is based on the image's histogram of M-LBP responses. Extensive experiments conducted on the CUReT database demonstrate the overall superiority of M-LBP over the other state-of-the-art methods evaluated.

引用

页码：2677 / 2680

页数：4

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