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|Title:||Image retrieval with query-adaptive hashing||Authors:||Liu, D.
|Keywords:||Algorithms||Issue Date:||Feb-2013||Citation:||Liu, D., Yan, S., Ji, R.-R., Hua, X.-S., Zhang, H.-J. (2013-02). Image retrieval with query-adaptive hashing. ACM Transactions on Multimedia Computing, Communications and Applications 9 (1) : -. ScholarBank@NUS Repository. https://doi.org/10.1145/2422956.2422958||Abstract:||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 accuracy. © 2013 ACM.||Source Title:||ACM Transactions on Multimedia Computing, Communications and Applications||URI:||http://scholarbank.nus.edu.sg/handle/10635/82492||ISSN:||15516857||DOI:||10.1145/2422956.2422958|
|Appears in Collections:||Staff Publications|
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