Dynamic texture recognition using enhanced LBP features

Jianfeng Ren, Xudong Jiang, Junsong Yuan

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

47 Citations (Scopus)

Abstract

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.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Proceedings
Pages2400-2404
Number of pages5
DOIs
Publication statusPublished - 18 Oct 2013
Externally publishedYes
Event2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013 - Vancouver, BC, Canada
Duration: 26 May 201331 May 2013

Publication series

NameICASSP, IEEE International Conference on Acoustics, Speech and Signal Processing - Proceedings
ISSN (Print)1520-6149

Conference

Conference2013 38th IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2013
Country/TerritoryCanada
CityVancouver, BC
Period26/05/1331/05/13

Keywords

  • Dynamic Texture Recognition
  • Local Binary Pattern
  • Principal Histogram Analysis
  • Super Histogram

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Electrical and Electronic Engineering

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