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Proceedings Paper

Transform coding of image feature descriptors
Author(s): Vijay Chandrasekhar; Gabriel Takacs; David Chen; Sam S. Tsai; Jatinder Singh; Bernd Girod
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Paper Abstract

We investigate transform coding to efficiently store and transmit SIFT and SURF image descriptors. We show that image and feature matching algorithms are robust to significantly compressed features. We achieve near-perfect image matching and retrieval for both SIFT and SURF using ~2 bits/dimension. When applied to SIFT and SURF, this provides a 16× compression relative to conventional floating point representation. We establish a strong correlation between MSE and matching error for feature points and images. Feature compression enables many application that may not otherwise be possible, especially on mobile devices.

Paper Details

Date Published: 19 January 2009
PDF: 9 pages
Proc. SPIE 7257, Visual Communications and Image Processing 2009, 725710 (19 January 2009); doi: 10.1117/12.805982
Show Author Affiliations
Vijay Chandrasekhar, Stanford Univ. (United States)
Gabriel Takacs, Stanford Univ. (United States)
David Chen, Stanford Univ. (United States)
Sam S. Tsai, 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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