DYNAMIC TEXTURE RECOGNITION USING ENHANCED LBP FEATURES

被引：0

作者：

Ren, Jianfeng ^{[1
]}

Jiang, Xudong ^{[2
]}

Yuan, Junsong ^{[2
]}

机构：

[1] Nanyang Technol Univ, Inst Media Innovat, BeingThere Ctr, 50 Nanyang Dr, Singapore 637553, Singapore

[2] Nanyang Technol Univ, Sch Elect & Elect Engn, Singapore 639798, Singapore

来源：

2013 IEEE INTERNATIONAL CONFERENCE ON ACOUSTICS, SPEECH AND SIGNAL PROCESSING (ICASSP) | 2013年

基金：

新加坡国家研究基金会;

关键词：

Local Binary Pattern; Dynamic Texture Recognition; Super Histogram; Principal Histogram Analysis; LOCAL BINARY PATTERNS; CLASSIFICATION;

D O I：

暂无

中图分类号：

O42 [声学];

学科分类号：

070206 ; 082403 ;

摘要：

This paper addresses the challenge of recognizing dynamic textures based on spatial-temporal descriptors. Dynamic textures are composed of both spatial and temporal features. The histogram of local binary pattern (LBP) has been used in dynamic texture recognition. However, its performance is limited by the reliability issues of the LBP histograms. In this paper, two learning-based approaches are proposed to remove the unreliable information in LBP features by utilizing Principal Histogram Analysis. Furthermore, a super histogram is proposed to improve the reliability of the LBP histograms. The temporal information is partially transferred to the super histogram. The proposed approaches are evaluated on two widely used benchmark databases: UCLA and Dyntex++ databases. Superior performance is demonstrated compared with the state of the arts.

引用

页码：2400 / 2404

页数：5

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