Similarity Retrieval Based on SOM-Based R*-Tree

  • K. H. Choi
  • M. H. Shin
  • S. H. Bae
  • C. H. Kwon
  • I. H. Ra
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3038)


Feature-based similarity retrieval has become an important research issue in multimedia database systems. The features of multimedia data are usually high-dimensional data. The performance of conventional multidimensional data structures tends to deteriorate as the number of dimensions of feature vectors increases. In this paper, we propose a SOM-based R*-tree(SBR-Tree) as a new indexing method for high-dimensional feature vectors. The SBR-Ttree combines SOM and R*-tree to achieve search performance more scalable to high dimensionalities. When we build an R*-tree, we use codebook vectors of topological feature map which eliminates the empty nodes that cause unnecessary disk access and degrade retrieval performance. We experimentally compare the retrieval time cost of a SBR – Tree with that of an SOM and an R*-tree using color feature vectors extracted from 40,000 images. The result show that the SOM-based R*-tree outperforms both the SOM and R*-tree due to the reduction of the number of nodes required to build R*-tree and retrieval time cost.


Feature Vector Index Structure Haar Wavelet Important Research Issue Empty Node 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • K. H. Choi
    • 1
  • M. H. Shin
    • 1
  • S. H. Bae
    • 1
  • C. H. Kwon
    • 2
  • I. H. Ra
    • 3
  1. 1.Computer Science & StatisticsChosun UniversityDong-Gu KwangjuKorea
  2. 2.Division of IT, Computer EngineeringHansei UniversityKyunggi-doKorea
  3. 3.Depts. Electronic and Information EngineeringKunsan NationalKorea

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