CASIA OpenIR  > 模式识别国家重点实验室  > 图像与视频分析
Semi-supervised multi-graph hashing for scalable similarity search
Cheng, Jian1; Leng, Cong1; Li, Peng1; Wang, Meng2; Lu, Hanqing1; Jian Cheng
Source PublicationCOMPUTER VISION AND IMAGE UNDERSTANDING
2014-07-01
Volume124Issue:1Pages:12-21
SubtypeArticle
AbstractDue 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.
KeywordHashing Multiple Graph Learning Multiple Modality Semi-supervised Learning
WOS HeadingsScience & Technology ; Technology
Indexed BySCI
Language英语
WOS Research AreaComputer Science ; Engineering
WOS SubjectComputer Science, Artificial Intelligence ; Engineering, Electrical & Electronic
WOS IDWOS:000337663600003
Citation statistics
Cited Times:20[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/3334
Collection模式识别国家重点实验室_图像与视频分析
Corresponding AuthorJian Cheng
Affiliation1.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
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
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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