Please use this identifier to cite or link to this item:
|Title:||Weakly-supervised hashing in kernel space||Authors:||Mu, Y.
|Issue Date:||2010||Citation:||Mu, Y., Shen, J., Yan, S. (2010). Weakly-supervised hashing in kernel space. Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition : 3344-3351. ScholarBank@NUS Repository. https://doi.org/10.1109/CVPR.2010.5540024||Abstract:||The explosive growth of the vision data motivates the recent studies on efficient data indexing methods such as locality-sensitive hashing (LSH). Most existing approaches perform hashing in an unsupervised way. In this paper we move one step forward and propose a supervised hashing method, i.e., the LAbel-regularized Max-margin Partition (LAMP) algorithm. The proposed method generates hash functions in weakly-supervised setting, where a small portion of sample pairs are manually labeled to be "similar" or "dissimilar". We formulate the task as a Constrained Convex-Concave Procedure (CCCP), which can be relaxed into a series of convex sub-problems solvable with efficient Quadratic-Program (QP). The proposed hashing method possesses other characteristics including: 1) most existing LSH approaches rely on linear feature representation. Unfortunately, kernel tricks are often more natural to gauge the similarity between visual objects in vision research, which corresponds to probably infinite-dimensional Hilbert spaces. The proposed LAMP has a natural support for kernel-based feature representation. 2) traditional hashing methods assume uniform data distributions. Typically, the collision probability of two samples in hash buckets is only determined by pairwise similarity, unrelated to contextual data distribution. In contrast, we provide such a collision bound which is beyond pairwise data interaction based on Markov random fields theory. Extensive empirical evaluations are conducted on five widely-used benchmarks. It takes only several seconds to generate a new hashing function, and the adopted random supporting-vector scheme enables the LAMP algorithm scalable to large-scale problems. Experimental results well validate the superiorities of the LAMP algorithm over the state-of-the-art kernel-based hashing methods. ©2010 IEEE.||Source Title:||Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition||URI:||http://scholarbank.nus.edu.sg/handle/10635/72185||ISBN:||9781424469840||ISSN:||10636919||DOI:||10.1109/CVPR.2010.5540024|
|Appears in Collections:||Staff Publications|
Show full item record
Files in This Item:
There are no files associated with this item.
checked on Dec 12, 2019
WEB OF SCIENCETM
checked on Dec 12, 2019
checked on Dec 1, 2019
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.