Semi-supervised multi-graph hashing for scalable similarity search

	Semi-supervised multi-graph hashing for scalable similarity search
	Cheng, Jian1 ; Leng, Cong1 ; Li, Peng1 ; Wang, Meng2 ; Lu, Hanqing1 ; Jian Cheng
发表期刊	COMPUTER VISION AND IMAGE UNDERSTANDING
	2014-07-01
卷号	124 期号:1 页码:12-21
文章类型	Article
摘要	Due to the explosive growth of the multimedia contents in recent years, scalable similarity search has attracted considerable attention in many large-scale multimedia applications. Among the different similarity search approaches, hashing based approximate nearest neighbor (ANN) search has become very popular owing to its computational and storage efficiency. However, most of the existing hashing methods usually adopt a single modality or integrate multiple modalities simply without exploiting the effect of different features. To address the problem of learning compact hashing codes with multiple modality, we propose a semi-supervised Multi-Graph Hashing (MGH) framework in this paper. Different from the traditional methods, our approach can effectively integrate the multiple modalities with optimized weights in a multi-graph learning scheme. In this way, the effects of different modalities can be adaptively modulated. Besides, semi-supervised information is also incorporated into the unified framework and a sequential learning scheme is adopted to learn complementary hash functions. The proposed framework enables direct and fast handling for the query examples. Thus, the binary codes, learned by our approach can be more effective for fast similarity search. Extensive experiments are conducted on two large public datasets to evaluate the performance of our approach and the results demonstrate that the proposed approach achieves promising results compared to the state-of-the-art methods. (C) 2014 Elsevier Inc. All rights reserved.
关键词	Hashing Multiple Graph Learning Multiple Modality Semi-supervised Learning
WOS标题词	Science & Technology ; Technology
收录类别	SCI
语种	英语
WOS研究方向	Computer Science ; Engineering
WOS类目	Computer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS记录号	WOS:000337663600003
引用统计	被引频次：28[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/3334
专题	紫东太初大模型研究中心_图像与视频分析
通讯作者	Jian Cheng
作者单位	1.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China 2.Hefei Univ Technol, Sch Comp & Informat, Hefei 230009, Peoples R China
第一作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	Cheng, Jian,Leng, Cong,Li, Peng,et al. Semi-supervised multi-graph hashing for scalable similarity search[J]. COMPUTER VISION AND IMAGE UNDERSTANDING,2014,124(1):12-21.
APA	Cheng, Jian,Leng, Cong,Li, Peng,Wang, Meng,Lu, Hanqing,&Jian Cheng.(2014).Semi-supervised multi-graph hashing for scalable similarity search.COMPUTER VISION AND IMAGE UNDERSTANDING,124(1),12-21.
MLA	Cheng, Jian,et al."Semi-supervised multi-graph hashing for scalable similarity search".COMPUTER VISION AND IMAGE UNDERSTANDING 124.1(2014):12-21.

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