Similarity retrieval based on self-organizing maps

  • Authors:

  • Dong-Ju Im;Malrey Lee;Young Keun Lee;Tae-Eun Kim;SuWon Lee;Jae-Wan Lee;Keun Kwang Lee;kyung Dal Cho

  • Affiliations:

  • Dept. of Multimedia, Yosu National University, Korea;School of Electronics & Information Engineering, Chonbuk National University, CnonBuk, Korea;Dept. of Orthopedic Surgery, Chonbuk National University Hospital;Dept. of Multimedia, Namseoul University, Korea;Dept. of Information Communication, KunSan National University, Korea;Dept. of Information Communication, KunSan National University, Korea;Dept of Skin Beauty Art, Naju Collage;Dept of Computer Science, Chung-Aang Unicersity

  • Venue:
  • ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part II
  • Year:
  • 2005

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Abstract

The features of image data are useful to discrimination of images. In this paper, we propose the high speed k-Nearest Neighbor search algorithm based on Self-Organizing Maps. Self-Organizing Maps provides a mapping from high dimensional feature vectors onto a two-dimensional space. The mapping preserves the topology of the feature vectors. The map is called topological feature map. A topological feature map preserves the mutual relations in feature spaces of input data. and clusters mutually similar feature vectors in a neighboring nodes. Each node of the topological feature map holds a node vector and similar images that is closest to each node vector. In topological feature map, there are empty nodes in which no image is classified. We experiment on the performance of our algorithm using color feature vectors extracted from images.