Learning LBP structure by maximizing the conditional mutual information

Jianfeng Ren, Xudong Jiang, Junsong Yuan

Research output: Journal PublicationArticlepeer-review

51 Citations (Scopus)

Abstract

Local binary patterns of more bits extracted in a large structure have shown promising results in visual recognition applications. This results in very high-dimensional data so that it is not feasible to directly extract features from the LBP histogram, especially for a large-scale database. Instead of extracting features from the LBP histogram, we propose a new approach to learn discriminative LBP structures for a specific application. Our objective is to select an optimal subset of binarized-pixel-difference features to compose the LBP structure. As these features are strongly correlated, conventional feature-selection methods may not yield a desirable performance. Thus, we propose an incremental Maximal-Conditional-Mutual-Information scheme for LBP structure learning. The proposed approach has demonstrated a superior performance over the state-of-the-arts results on classifying both spatial patterns such as texture classification, scene recognition and face recognition, and spatial-temporal patterns such as dynamic texture recognition.

Original languageEnglish
Pages (from-to)3180-3190
Number of pages11
JournalPattern Recognition
Volume48
Issue number10
DOIs
Publication statusPublished - 1 Oct 2015
Externally publishedYes

Keywords

  • Dynamic texture recognition
  • Face recognition
  • LBP structure learning
  • Maximal conditional mutual information
  • Scene recognition

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'Learning LBP structure by maximizing the conditional mutual information'. Together they form a unique fingerprint.

Cite this