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Proceedings Paper

Robust image retrieval using multiview scalable vocabulary trees
Author(s): David Chen; Sam S. Tsai; Vijay Chandrasekhar; Gabriel Takacs; Jatinder Singh; Bernd Girod
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Paper Abstract

Content-based image retrieval using a Scalable Vocabulary Tree (SVT) built from local scale-invariant features is an effective method of fast search through a database. An SVT built from fronto-parallel database images, however, is ineffective at classifying query images that suffer from perspective distortion. In this paper, we propose an efficient server-side extension of the single-view SVT to a set of multiview SVTs that may be simultaneously employed for image classification. Our solution results in significantly better retrieval performance when perspective distortion is present. We also develop an analysis of how perspective increases the distance between matching query-database feature descriptors.

Paper Details

Date Published: 19 January 2009
PDF: 9 pages
Proc. SPIE 7257, Visual Communications and Image Processing 2009, 72570V (19 January 2009); doi: 10.1117/12.805606
Show Author Affiliations
David Chen, Stanford Univ. (United States)
Sam S. Tsai, Stanford Univ. (United States)
Vijay Chandrasekhar, Stanford Univ. (United States)
Gabriel Takacs, Stanford Univ. (United States)
Jatinder Singh, Deutsche Telekom Labs. (United States)
Bernd Girod, Stanford Univ. (United States)


Published in SPIE Proceedings Vol. 7257:
Visual Communications and Image Processing 2009
Majid Rabbani; Robert L. Stevenson, Editor(s)

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