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Modeling spatial and semantic cues for large-scale near-duplicated image retrieval

文献类型：期刊论文

作者Zhang, Shiliang5; Tian, Qi4; Hua, Gang1; Zhou, Wengang3; Huang, Qingming2; Li, Houqiang3; Gao, Wen5
刊名COMPUTER VISION AND IMAGE UNDERSTANDING
出版日期2011-03-01
卷号115期号:3页码:403-414
关键词Visual vocabulary Near-duplicated image retrieval Local feature Distance metric learning
ISSN号1077-3142
DOI10.1016/j.cviu.2010.11.003
英文摘要Bag-of-visual Words (BOW) image representation has been illustrated as one of the most promising solutions for large-scale near-duplicated image retrieval. However, the traditional visual vocabulary is created in an unsupervised way by clustering a large number of image local features. This is not ideal because it largely ignores the semantic and spatial contexts between local features. In this paper, we propose the geometric visual vocabulary which captures the spatial contexts by quantizing local features in bi-space, i.e., in descriptor space and orientation space. Then, we propose to capture the semantic context by learning a semantic-aware distance metric between local features, which could reasonably measure the semantic similarities between image patches, from which the local features are extracted. The learned distance is hence utilized to cluster the local features for semantic visual vocabulary generation. Finally, we combine the spatial and semantic contexts in a unified framework by extracting local feature groups, computing the spatial configurations between the local features inside the group, and learning a semantic-aware distance between groups. The learned group distance is then utilized to cluster the extracted local feature groups to generate a novel visual vocabulary. i.e., the contextual visual vocabulary. The proposed visual vocabularies, i.e., geometric visual vocabulary, semantic visual vocabulary and contextual visual vocabulary are tested in large-scale near-duplicated image retrieval applications. The geometric visual vocabulary and semantic visual vocabulary achieve better performance than the traditional visual vocabulary. Moreover, the contextual visual vocabulary, which combines both spatial and semantic clues outperforms the state-of-the-art bundled feature in both retrieval precision and efficiency. (C) 2010 Elsevier Inc. All rights reserved.
资助项目National Natural Science Foundation of China[61025011] ; National Natural Science Foundation of China[60833006] ; National Basic Research Program of China (973 Program)[2009CB320906] ; Beijing Natural Science Foundation[4092042] ; NSF IIS[1052851] ; Akiira Media Systems, Inc.
WOS研究方向Computer Science ; Engineering
语种英语
WOS记录号WOS:000287772400011
出版者ACADEMIC PRESS INC ELSEVIER SCIENCE
源URL[http://119.78.100.204/handle/2XEOYT63/12756]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Tian, Qi
作者单位1.IBM Watson Res Ctr, Elmsford, NY 10523 USA
2.Chinese Acad Sci, Grad Univ, Beijing 100049, Peoples R China
3.Univ Sci & Technol China, Dept EEIS, Hefei 230026, Peoples R China
4.Univ Texas San Antonio, Dept Comp Sci, San Antonio, TX 78249 USA
5.CAS, Inst Comput Tech, Key Lab Intell Info Proc, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Shiliang,Tian, Qi,Hua, Gang,et al. Modeling spatial and semantic cues for large-scale near-duplicated image retrieval[J]. COMPUTER VISION AND IMAGE UNDERSTANDING,2011,115(3):403-414.
APA Zhang, Shiliang.,Tian, Qi.,Hua, Gang.,Zhou, Wengang.,Huang, Qingming.,...&Gao, Wen.(2011).Modeling spatial and semantic cues for large-scale near-duplicated image retrieval.COMPUTER VISION AND IMAGE UNDERSTANDING,115(3),403-414.
MLA Zhang, Shiliang,et al."Modeling spatial and semantic cues for large-scale near-duplicated image retrieval".COMPUTER VISION AND IMAGE UNDERSTANDING 115.3(2011):403-414.

入库方式： OAI收割

来源：计算技术研究所

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