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Region-Based Image Clustering and Retrieval Using Multiple Instance Learning

  • Chengcui Zhang
  • Xin Chen
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3568)

Abstract

Multiple Instance Learning (MIL) is a special kind of supervised learning problem that has been studied actively in recent years. We propose an approach based on One-Class Support Vector Machine (SVM) to solve MIL problem in the region-based Content Based Image Retrieval (CBIR). This is an area where a huge number of image regions are involved. For the sake of efficiency, we adopt a Genetic Algorithm based clustering method to reduce the search space. Relevance Feedback technique is incorporated to provide progressive guidance to the learning process. Performance is evaluated and the effectiveness of our retrieval algorithm is demonstrated in comparative studies.

Keywords

Genetic Algorithm Image Retrieval Image Region Relevance Feedback Query Result 
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 2005

Authors and Affiliations

  • Chengcui Zhang
    • 1
  • Xin Chen
    • 1
  1. 1.Department of Computer and Information SciencesUniversity of Alabama at Birmingham 

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