A Latent Semantic Indexing Based Method for Solving Multiple Instance Learning Problem in Region-Based Image Retrieval

  • Authors:

  • Xin Chen;Chengcui Zhang;Shu-Ching Chen;Min Chen

  • Affiliations:

  • University of Alabama at Birmingham;University of Alabama at Birmingham;Florida International University;Florida International University

  • Venue:
  • ISM '05 Proceedings of the Seventh IEEE International Symposium on Multimedia
  • Year:
  • 2005

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Abstract

Relevance Feedback (RF) is a widely used technique in incorporating user's knowledge with the learning process for Content-Based Image Retrieval (CBIR). As a supervised learning technique, it has been shown to significantly increase the retrieval accuracy. However, as a CBIR system continues to receive user queries and user feedbacks, the information of user preferences across query sessions are often lost at the end of search, thus requiring the feedback process to be restarted for each new query. A few works targeting long-term learning have been done in general CBIR domain to alleviate this problem. However, none of them address the needs and long-term similarity learning techniques for regionbased image retrieval. This paper proposes a Latent Semantic Indexing (LSI) based method to utilize users' relevance feedback information. The proposed regionbased image retrieval system is constructed on a Multiple Instance Learning (MIL) framework with One-class Support Vector Machine (SVM) as its core. Experiments show that the proposed method can better utilize users' feedbacks of previous sessions, thus improving the performance of the learning algorithm (One-class SVM).