Expert system for color image retrieval

Hun Woo Yoo, Han Soo Park, Dong Sik Jang

Research output: Contribution to journalArticlepeer-review

28 Citations (Scopus)


Recently, as Web and various databases contain a large number of images, content-based image retrieval (CBIR) applications are greatly needed. This paper proposes a new image retrieval system using color-spatial information from those applications. First, this paper suggests two kinds of indexing keys to prune away irrelevant images to a given query image: major colors' set (MCS) signature related with color information and distribution block signature (DBS) related with spatial information. After successively applying these filters to a large database, we get only small amount of high potential candidates that are somewhat similar to a query image. Then we make use of the quad modeling (QM) method to set the initial weights of two-dimensional cell in a query image according to each major color. Finally, we retrieve more similar images from the database by comparing a query image with candidate images through a similarity measuring function associated with the weights. In that procedure, we use a new relevance feedback mechanism. This feedback enhances the retrieval effectiveness by dynamically modulating the weights of color-spatial information. Experiments show that the proposed system is not only efficient but also effective.

Original languageEnglish
Pages (from-to)347-357
Number of pages11
JournalExpert Systems With Applications
Issue number2
Publication statusPublished - 2005 Feb

Bibliographical note

Copyright 2011 Elsevier B.V., All rights reserved.


  • CBIR (content-based image retrieval)
  • DBS (distribution block signature)
  • MCS (major colors' set) signature
  • QM (quad modeling)
  • Relevance feedback

ASJC Scopus subject areas

  • General Engineering
  • Computer Science Applications
  • Artificial Intelligence


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