A Chi-Squared-Transformed Subspace of LBP Histogram for Visual Recognition

Jianfeng Ren, Xudong Jiang, Junsong Yuan

Research output: Journal PublicationArticlepeer-review

27 Citations (Scopus)


Local binary pattern (LBP) and its variants have been widely used in many recognition tasks. Subspace approaches are often applied to the LBP feature in order to remove unreliable dimensions, or to derive a compact feature representation. It is well-known that subspace approaches utilizing up to the second-order statistics are optimal only when the underlying distribution is Gaussian. However, due to its nonnegative and simplex constraints, the LBP feature deviates significantly from Gaussian distribution. To alleviate this problem, we propose a chi-squared transformation (CST) to transfer the LBP feature to a feature that fits better to Gaussian distribution. The proposed CST leads to the formulation of a two-class classification problem. Due to its asymmetric nature, we apply asymmetric principal component analysis (APCA) to better remove the unreliable dimensions in the CST feature space. The proposed CST-APCA is evaluated extensively on spatial LBP for face recognition, protein cellular classification, and spatial-temporal LBP for dynamic texture recognition. All experiments show that the proposed feature transformation significantly enhances the recognition accuracy.

Original languageEnglish
Article number7055908
Pages (from-to)1893-1904
Number of pages12
JournalIEEE Transactions on Image Processing
Issue number6
Publication statusPublished - 1 Jun 2015
Externally publishedYes


  • Asymmetric PCA
  • Chi-squared Transformation
  • Image Recognition
  • Local Binary Pattern
  • Truncated Gaussian Model

ASJC Scopus subject areas

  • Software
  • Computer Graphics and Computer-Aided Design


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