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Proceedings Paper

Compressing a set of CHoG features
Author(s): Vijay Chandrasekhar; Sam S. Tsai; Yuriy Reznik; Gabriel Takacs; David M. Chen; Bernd Girod
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Paper Abstract

State-of-the-art image retrieval pipelines are based on "bag-of-words" matching. We note that the original order in which features are extracted from the image is discarded in the "bag-of-words" matching pipeline. As a result, a set of features extracted from a query image can be transmitted in any order. A set ofm unique features has m! orderings, and if the order of transmission can be discarded, one can reduce the query size by an additional log2(m!) bits. In this work, we compare two schemes for discarding ordering: one based on Digital Search Trees, and another based on location histograms. We apply the two schemes to a set of low bitrate Compressed Histogram of Gradient (CHoG) features, and compare their performance. Both schemes achieve approximately log2(m!) reduction in query size for a set of m features.

Paper Details

Date Published: 24 September 2011
PDF: 12 pages
Proc. SPIE 8135, Applications of Digital Image Processing XXXIV, 813517 (24 September 2011); doi: 10.1117/12.895431
Show Author Affiliations
Vijay Chandrasekhar, Stanford Univ. (United States)
Sam S. Tsai, Stanford Univ. (United States)
Yuriy Reznik, Qualcomm Inc. (United States)
Gabriel Takacs, Stanford Univ. (United States)
David M. Chen, Stanford Univ. (United States)
Bernd Girod, Stanford Univ. (United States)

Published in SPIE Proceedings Vol. 8135:
Applications of Digital Image Processing XXXIV
Andrew G. Tescher, Editor(s)

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