Please use this identifier to cite or link to this item: https://doi.org/10.1109/CVPR.2013.64
Title: Compressed hashing
Authors: Lin, Y.
Jin, R.
Cai, D.
Yan, S. 
Li, X.
Keywords: Compressed Sensing
Hashing
Nearest Neighbor Search
Random Projection
Issue Date: 2013
Source: Lin, Y., Jin, R., Cai, D., Yan, S., Li, X. (2013). Compressed hashing. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition : 446-451. ScholarBank@NUS Repository. https://doi.org/10.1109/CVPR.2013.64
Abstract: Recent studies have shown that hashing methods are effective for high dimensional nearest neighbor search. A common problem shared by many existing hashing methods is that in order to achieve a satisfied performance, a large number of hash tables (i.e., long code-words) are required. To address this challenge, in this paper we propose a novel approach called Compressed Hashing by exploring the techniques of sparse coding and compressed sensing. In particular, we introduce as parse coding scheme, based on the approximation theory of integral operator, that generate sparse representation for high dimensional vectors. We then project s-parse codes into a low dimensional space by effectively exploring the Restricted Isometry Property (RIP), a key property in compressed sensing theory. Both of the theoretical analysis and the empirical studies on two large data sets show that the proposed approach is more effective than the state-of-the-art hashing algorithms. © 2013 IEEE.
Source Title: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
URI: http://scholarbank.nus.edu.sg/handle/10635/69677
ISSN: 10636919
DOI: 10.1109/CVPR.2013.64
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