Visual information retrieval system via content-based approach

Hun Woo Yoo, Dong Sik Jang, Seh Hwan Jung, Jin Hyung Park, Kwang Seop Song

Research output: Contribution to journalArticlepeer-review

46 Citations (Scopus)


In this paper a content-based image retrieval method that can search large image databases efficiently by color, texture, and shape content is proposed. Quantized RGB histograms and the dominant triple (hue, saturation, and value), which are extracted from quantized HSV joint histogram in the local image region, are used for representing global/local color information in the image. Entropy and maximum entry from co-occurrence matrices are used for texture information and edge angle histogram is used for representing shape information. Relevance feedback approach, which has coupled proposed features, is used for obtaining better retrieval accuracy. A new indexing method that supports fast retrieval in large image databases is also presented. Tree structures constructed by k-means algorithm, along with the idea of triangle inequality, eliminate candidate images for similarity calculation between query image and each database image. We find that the proposed method reduces calculation up to average 92.2 percent of the images from direct comparison.

Original languageEnglish
Pages (from-to)749-769
Number of pages21
JournalPattern Recognition
Issue number3
Publication statusPublished - 2002 Mar


  • Color
  • Content-based
  • Features
  • Image retrieval
  • Relevance feedback
  • Shape
  • Texture
  • Tree
  • Triangle inequality

ASJC Scopus subject areas

  • Software
  • Signal Processing
  • Computer Vision and Pattern Recognition
  • Artificial Intelligence


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