A dynamic user concept pattern learning framework for content-based image retrieval

Shu Ching Chen, Stuart H. Rubin, Mei Ling Shyu, Chengcui Zhang

Research output: Contribution to journalArticlepeer-review

41 Scopus citations


A rapid increase in the amount of image data and the inefficiency of traditional text-based image retrieval systems have served to make content-based image retrieval an active research field. It is crucial to effectively discover users' concept patterns through an acquired understanding of the subjective role played by humans in the retrieval process for such systems. A learning and retrieval framework is used to achieve this. It seamlessly incorporates multiple instance learning for relevant feedback to discover users concept patterns - especially in the region of greatest user interest. It also maps the local feature vector of that region to the high-level concept pattern. This underlying mapping can be progressively discovered through feedback and learning. The user guides the retrieval systems learning process using his/her focus of attention. Retrieval performance is tested to establish the feasibility and effectiveness of the proposed learning and retrieval framework.

Original languageEnglish (US)
Pages (from-to)772-783
Number of pages12
JournalIEEE Transactions on Systems, Man and Cybernetics Part C: Applications and Reviews
Issue number6
StatePublished - Nov 2006


  • Content-based image retrieval (CBIR)
  • Multiple instance learning
  • Neural network
  • Relevance feedback

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Artificial Intelligence
  • Human-Computer Interaction
  • Computer Science Applications
  • Computational Theory and Mathematics


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