Learning binarized pixel-difference pattern for scene recognition

Jianfeng Ren, Xudong Jiang, Junsong Yuan

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

11 Citations (Scopus)

Abstract

Local binary pattern (LBP) and its variants have been used in scene recognition. However, most existing approaches rely on a pre-defined LBP structure to extract features. Those pre-defined structures can be generalized as the patterns constructed from the binarized pixel differences in a local neighborhood. Instead of using a handcraft structure, we propose to learn binarized pixel-difference patterns (BPP). We cast the problem as a feature selection problem and solve it by an incremental search via the criterion of minimum-redundancy-maximum-relevance. Then, BPP features are extracted based on the structures derived. On two challenging scene recognition databases, the proposed approach significantly outperforms the state of the arts.

Original languageEnglish
Title of host publication2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings
PublisherIEEE Computer Society
Pages2494-2498
Number of pages5
ISBN (Print)9781479923410
DOIs
Publication statusPublished - 2013
Externally publishedYes
Event2013 20th IEEE International Conference on Image Processing, ICIP 2013 - Melbourne, VIC, Australia
Duration: 15 Sep 201318 Sep 2013

Publication series

Name2013 IEEE International Conference on Image Processing, ICIP 2013 - Proceedings

Conference

Conference2013 20th IEEE International Conference on Image Processing, ICIP 2013
Country/TerritoryAustralia
CityMelbourne, VIC
Period15/09/1318/09/13

Keywords

  • Binarized Pixel-difference Pattern
  • Feature Selection
  • Local Binary Pattern
  • Scene Recognition

ASJC Scopus subject areas

  • Computer Vision and Pattern Recognition

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