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An Efficient Index Structure for High Dimensional Image Data

  • Jae Soo Yoo
  • Myung Keun Shin
  • Seok Hee Lee
  • Kil Seong Choi
  • Ki Hyung Cho
  • Dae Young Hur
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 1554)

Abstract

The existing multi-dimensional index structures are not adequate for indexing higher-dimensional data sets. Although conceptually they can be extended to higher dimensionalities, they usually require time and space that grow exponentially with the dimensionality. In this paper, we analyze the existing index structures and derive some requirements of an index structure for content-based image retrieval. We also propose a new structure, called CIR(Content-based Image Retrieval)-tree, for indexing large amounts of point data in high dimensional space that satisfies the requirements. In order to justify the performance of the proposed structure, we compare the proposed structure with the existing index structures in the various environments. We show through experiments that our proposed structure outperforms the existing structures in terms of retrieval time and storage overhead.

Keywords

Feature Vector Leaf Node Index Structure Parent Node High Dimensional Data 
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 1999

Authors and Affiliations

  • Jae Soo Yoo
    • 1
  • Myung Keun Shin
    • 2
  • Seok Hee Lee
    • 1
  • Kil Seong Choi
    • 1
  • Ki Hyung Cho
    • 1
  • Dae Young Hur
    • 3
  1. 1.Department of Computer & Communication EngineeringChungbuk National UniversityChungbukSouth Korea
  2. 2.CAIS, KAISTTaejonSouth Korea
  3. 3.Database sectionETRITaejonSouth Korea

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