Weakly-Supervised Hashing in Kernel Space

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Weakly-Supervised Hashing in Kernel Space
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 prob...
Yadong Mu, Jialie Shen, Shuicheng Yan
Added 23 Jun 2010
Updated 23 Jun 2010
Type Conference
Year 2010
Where CVPR
Authors Yadong Mu, Jialie Shen, Shuicheng Yan
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