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Proceedings Paper

Hierarchical indexing scheme for fast search in a large-scale image database
Author(s): Hangjun Ye; Guangyou Xu
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Paper Abstract

Practical content-based image retrieval systems require efficient indexing schemes for fast k-nearest neighbor (k-NN) searches. Researchers have proposed many tree-based methods using space and data partitioning for similarity searches. However, traditional indexing methods perform poorly and will degrade to simple sequential scans at high dimensionality - that is so-called "curse of dimensionality". Recently, several filtering approaches based on vector approximation (VA) were proposed and showed promising performance. However, VA-based approaches need compute the bound of the distance between each feature vector and the query. It will consume the same computational overhead as the brute-force sequential scan. In this paper, a novel hierarchical indexing scheme is proposed. This approach integrates VA-based index structure with approximate NN (ANN) searches and performs probabilistic ANN searches on approximate vectors. Experiments show the proposed approach achieves a remarkable reduction of computational overhead and disk accesses for k-NN searches. This presented approach supports quadratic-form distance metric and can integrate with relevance feedback techniques for practical large-scale image retrieval systems.

Paper Details

Date Published: 25 September 2003
PDF: 6 pages
Proc. SPIE 5286, Third International Symposium on Multispectral Image Processing and Pattern Recognition, (25 September 2003); doi: 10.1117/12.539947
Show Author Affiliations
Hangjun Ye, Tsinghua Univ. (China)
Guangyou Xu, Tsinghua Univ. (China)


Published in SPIE Proceedings Vol. 5286:
Third International Symposium on Multispectral Image Processing and Pattern Recognition
Hanqing Lu; Tianxu Zhang, Editor(s)

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