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
收藏
页码：2677 / 2680
页数：4
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