Combining self-organizing neural nets with multivariate statistics for efficient color image retrieval

  • Authors:
  • Christos Theoharatos;Nikolaos Laskaris;George Economou;Spiros Fotopoulos

  • Affiliations:
  • Electronics Laboratory, Department of Physics, University of Patras, Patras, Greece;Artificial Intelligence and Information Analysis Laboratory, Department of Informatics, Aristotle University of Thessaloniki, Thessaloniki, Greece;Electronics Laboratory, Department of Physics, University of Patras, Patras, Greece;Electronics Laboratory, Department of Physics, University of Patras, Patras, Greece

  • Venue:
  • Computer Vision and Image Understanding
  • Year:
  • 2006

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Abstract

An efficient novel strategy for color-based image retrieval is introduced. It is a hybrid approach combining a data compression scheme based on self-organizing neural networks with a nonparametric statistical test for comparing vectorial distributions. First, the color content in each image is summarized by representative RGB-vectors extracted using the Neural-Gas network. The similarity between two images is then assessed as commonality between the corresponding representative color distributions and quantified using the multivariate Wald-Wolfowitz test. Experimental results drawn from the application to a diverse collection of color images show a significantly improved performance (approximately 10-15% higher) relative to both the popular, simplistic approach of color histogram and the sophisticated, computationally demanding technique of Earth Mover's Distance.