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Proceedings Paper

Improved coding for image feature location information
Author(s): Sam S. Tsai; David Chen; Gabriel Takacs; Vijay Chandrasekhar; Mina Makar; Radek Grzeszczuk; Bernd Girod
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Paper Abstract

In mobile visual search applications, an image-based query is typically sent from a mobile client to the server. Because of the bit-rate limitations, the query should be as small as possible. When performing image-based retrieval with local features, there are two types of information: the descriptors of the image features and the locations of the image features within the image. Location information can be used to check geometric consistency of the set of features and thus improve the retrieval performance. To compress the location information, location histogram coding is an effective solution. We present a location histogram coder that reduces the bitrate by 2:8x when compared to a fixed-rate scheme and 12:5x when compared to a floating point representation of the locations. A drawback is the large context table which can be difficult to store in the coder and requires large training data. We propose a new sum-based context for coding the location histogram map. We show that it can reduce the context up to 200x while being able to perform just as well as or better than previously proposed location histogram coders.

Paper Details

Date Published: 15 October 2012
PDF: 11 pages
Proc. SPIE 8499, Applications of Digital Image Processing XXXV, 84991E (15 October 2012); doi: 10.1117/12.935619
Show Author Affiliations
Sam S. Tsai, Stanford Univ. (United States)
David Chen, Stanford Univ. (United States)
Gabriel Takacs, Stanford Univ. (United States)
Vijay Chandrasekhar, Stanford Univ. (United States)
Mina Makar, Stanford Univ. (United States)
Radek Grzeszczuk, Nokia Research Ctr. (United States)
Bernd Girod, Stanford Univ. (United States)

Published in SPIE Proceedings Vol. 8499:
Applications of Digital Image Processing XXXV
Andrew G. Tescher, Editor(s)

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