Non-metric Similarity Ranking for Image Retrieval

  • Guang-Ho Cha
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4080)


Over many years, almost all research work in the content-based image retrieval has used Minkowski distance (or L p -norm) to measure similarity between images. However such functions cannot adequately capture the aspects of the characteristics of the human visual system. In this paper, we present a new similarity measure reflecting the nonlinearity of human perception. Based on this measure, we develop a similarity ranking algorithm for effective image retrieval. This algorithm exploits the inherent cluster structure revealed by an image dataset. Our method yields encouraging experimental results on a real image database and demonstrates its effectiveness.


Image Retrieval Query Image Relevance Feedback Query Point Ranking Algorithm 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Guang-Ho Cha
    • 1
  1. 1.Department of Computer EngineeringSeoul National University of TechnologySeoulSouth Korea

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