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A Novel Metrics Based on Information Bottleneck Principle for Face Retrieval

  • Qiyun Cai
  • Yuchun Fang
  • Jie Luo
  • Wang Dai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6297)

Abstract

In this paper, we propose a novel metrics for statistical features of images based on Information Bottleneck principle (IBP). Rather than measure the differences among images with classical distance, our model takes the attributes of feature space into consideration. Through evaluating the loss of information of image database, our model is especially designed for the type of features bearing statistical attributes such as histograms, moments etc. The statistical feature is adopted to denote the information of the image database and our metrics measures the distance between two images with the amount of decreased information due to combine them as one category. The proposed metrics is validated in face retrieval with the dominant Local Binary Pattern (LBP) feature. Experimental results on FERET face database show that our model possesses preferable performance.

Keywords

Face Image Local Binary Pattern Mahalanobis Distance Face Database Probability Mass Function 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2010

Authors and Affiliations

  • Qiyun Cai
    • 1
  • Yuchun Fang
    • 1
  • Jie Luo
    • 1
  • Wang Dai
    • 1
  1. 1.School of Computer Engineering and ScienceShanghai UniversityChina

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