MU, Yadong

SHEN, Jialie

YAN, Shuicheng

Crossref

Weakly-supervised hashing in kernel space

10.1109/CVPR.2010.5540024Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition3344-3351PIVR

Mu, Y.

Shen, J.

Yan, S.

ScholarBank@NUS

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 por-tion 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 charac-teristics including: 1) most existing LSH approaches rely on linear feature representation. Unfortunately, kernel tricks are often more natural to gauge the similarity between vi-sual objects in vision research, which corresponds to prob-ably infinite-dimensional Hilbert spaces. The proposed LAMP has a natural support for kernel-based feature repre-sentation. 2) traditional hashing methods assume uniform data distributions. Typically, the collision probability of two samples in hash buckets is only determined by pairwise sim-ilarity, unrelated to contextual data distribution. In con-trast, 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 ran-dom supporting-vector scheme enables the LAMP algo-rithm scalable to large-scale problems. Experimental re-sults well validate the superiorities of the LAMP algorithm over the state-of-the-art kernel-based hashing methods. 1

Weakly-Supervised Hashing in Kernel Space

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