Integrating Local One-Class Classifiers for Image Retrieval

  • Yiqing Tu
  • Gang Li
  • Honghua Dai
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4093)


In content-based image retrieval, learning from users’ feedback can be considered as an one-class classification problem. However, the OCIB method proposed in [1] suffers from the problem that it is only a one-mode method which cannot deal with multiple interest regions. In addition, it requires a pre-specified radius which is usually unavailable in real world applications. This paper overcomes these two problems by introducing ensemble learning into the OCIB method: by Bagging, we can construct a group of one-class classifiers which emphasize various parts of the data set; this is followed by a rank aggregating with which results from different parameter settings are incorporated into a single final ranking list. The experimental results show that the proposed I-OCIB method outperforms the OCIB for image retrieval applications.


Image Retrieval Decision Function Mean Average Precision Ranking List Relevant Image 
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 2006

Authors and Affiliations

  • Yiqing Tu
    • 1
  • Gang Li
    • 1
  • Honghua Dai
    • 1
  1. 1.School of Engineering and Information TechnologyDeakin UniversityAustralia

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