Relevance feedback techniques in interactive content-based image retrieval

Yong Rui, Thomas S Huang, Sharad Mehrotra

Research output: Contribution to journalConference articlepeer-review


Content-Based Image Retrieval (CBIR) has become one of the most active research areas in the past few years. Many visual feature representations have been explored and many systems built. While these research efforts establish the basis of CBIR, the usefulness of the proposed approaches is limited. Specifically, these efforts have relatively ignored two distinct characteristics of CBIR systems: (1) the gap between high level concepts and low level features; (2) subjectivity of human perception of visual content. This paper proposes a relevance feedback based interactive retrieval approach, which effectively takes into account the above two characteristics in CBIR. During the retrieval process, the user's high level query and perception subjectivity are captured by dynamically updated weights based on the user's relevance feedback. The experimental results show that the proposed approach greatly reduces the user's effort of composing a query and captures the user's information need more precisely.

Original languageEnglish (US)
Pages (from-to)25-36
Number of pages12
JournalProceedings of SPIE - The International Society for Optical Engineering
StatePublished - Dec 1 1998
EventStorage and Retrieval for Image and Video Databases VI - San Jose, CA, United States
Duration: Jan 28 1998Jan 30 1998

ASJC Scopus subject areas

  • Electronic, Optical and Magnetic Materials
  • Condensed Matter Physics
  • Computer Science Applications
  • Applied Mathematics
  • Electrical and Electronic Engineering

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