期刊
PATTERN RECOGNITION
卷 47, 期 2, 页码 748-757出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.patcog.2013.08.022
关键词
Locality-sensitive hashing; Multiple features; Compact hashing; Multiple kernels
资金
- National Major Project of China Advanced Unstructured Data Repository [2010ZX01042-002-001-00]
- SKLSDE Foundation [SKLSDE-2013ZX-05]
- NSFC [61370125]
Recently hashing has become attractive in large-scale visual search, owing to its theoretical guarantee and practical success. However, most of the state-of-the-art hashing methods can only employ a single feature type to learn hashing functions. Related research on image search, clustering, and other domains has proved the advantages of fusing multiple features. In this paper we propose a novel multiple feature kernel hashing framework, where hashing functions are learned to preserve certain similarities with linearly combined multiple kernels corresponding to different features. The framework is not only compatible with general types of data and diverse types of similarities indicated by different visual features, but also general for both supervised and unsupervised scenarios. We present efficient alternating optimization algorithms to learn both the hashing functions and the optimal kernel combination. Experimental results on three large-scale benchmarks CIFAR-10, NUS-WIDE and a-TRECVID show that the proposed approach can achieve superior accuracy and efficiency over state-of-the-art methods. (C) 2013 Elsevier Ltd. All rights reserved.
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