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Image retrieval with query-adaptive hashing

Image retrieval with query-adaptive hashing Image Retrieval with Query-Adaptive Hashing DONG LIU, Harbin Institute of Technology SHUICHENG YAN, National University of Singapore RONG-RONG JI, Harbin Institute of Technology XIAN-SHENG HUA, Microsoft Research Asia HONG-JIANG ZHANG, Microsoft Advanced Technology Center Hashing-based approximate nearest-neighbor search may well realize scalable content-based image retrieval. The existing semantic-preserving hashing methods leverage the labeled data to learn a fixed set of semantic-aware hash functions. However, a fixed hash function set is unable to well encode all semantic information simultaneously, and ignores the specific user's search intention conveyed by the query. In this article, we propose a query-adaptive hashing method which is able to generate the most appropriate binary codes for different queries. Specifically, a set of semantic-biased discriminant projection matrices are first learnt for each of the semantic concepts, through which a semantic-adaptable hash function set is learnt via a joint sparsity variable selection model. At query time, we further use the sparsity representation procedure to select the most appropriate hash function subset that is informative to the semantic information conveyed by the query. Extensive experiments over three benchmark image datasets well demonstrate the superiority of our proposed query-adaptive hashing method over the state-of-the-art ones in terms of retrieval http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) Association for Computing Machinery

Image retrieval with query-adaptive hashing

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2013 by ACM Inc.
ISSN
1551-6857
DOI
10.1145/2422956.2422958
Publisher site
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Abstract

Image Retrieval with Query-Adaptive Hashing DONG LIU, Harbin Institute of Technology SHUICHENG YAN, National University of Singapore RONG-RONG JI, Harbin Institute of Technology XIAN-SHENG HUA, Microsoft Research Asia HONG-JIANG ZHANG, Microsoft Advanced Technology Center Hashing-based approximate nearest-neighbor search may well realize scalable content-based image retrieval. The existing semantic-preserving hashing methods leverage the labeled data to learn a fixed set of semantic-aware hash functions. However, a fixed hash function set is unable to well encode all semantic information simultaneously, and ignores the specific user's search intention conveyed by the query. In this article, we propose a query-adaptive hashing method which is able to generate the most appropriate binary codes for different queries. Specifically, a set of semantic-biased discriminant projection matrices are first learnt for each of the semantic concepts, through which a semantic-adaptable hash function set is learnt via a joint sparsity variable selection model. At query time, we further use the sparsity representation procedure to select the most appropriate hash function subset that is informative to the semantic information conveyed by the query. Extensive experiments over three benchmark image datasets well demonstrate the superiority of our proposed query-adaptive hashing method over the state-of-the-art ones in terms of retrieval

Journal

ACM Transactions on Multimedia Computing, Communications, and Applications (TOMCCAP) – Association for Computing Machinery

Published: Feb 1, 2013

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