Expert system for color image retrieval

  • Authors:

  • Hun-Woo Yoo;Han-Soo Park;Dong-Sik Jang

  • Affiliations:

  • Center for Cognitive Science, Yonsei University, 134 Shinchon-Dong, Seodaemun-Ku, Seoul 120-749, South Korea;Department of Industrial Systems and Information Engineering, Korea University, Sungbuk-gu Anam-dong 5 Ga 1, Seoul 136-701, South Korea;Department of Industrial Systems and Information Engineering, Korea University, Sungbuk-gu Anam-dong 5 Ga 1, Seoul 136-701, South Korea

  • Venue:
  • Expert Systems with Applications: An International Journal
  • Year:
  • 2005

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Abstract

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.