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Location-Based Parallel Tag Completion for Geo-Tagged Social Image Retrieval

文献类型：期刊论文

作者Zhang, Jiaming1; Wang, Shuhui1; Huang, Qingming2
刊名ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY
出版日期2017-04-01
卷号8期号:3页码:21
关键词Tag matrix completion geo-location information social image retrieval asymmetric locality sensitive hashing
ISSN号2157-6904
DOI10.1145/3001593
英文摘要Having benefited from tremendous growth of user-generated content, social annotated tags get higher importance in the organization and retrieval of large-scale image databases on Online Sharing Websites (OSW). To obtain high-quality tags from existing community contributed tags with missing information and noise, tag-based annotation or recommendation methods have been proposed for performance promotion of tag prediction. While images from OSW contain rich social attributes, they have not taken full advantage of rich social attributes and auxiliary information associated with social images to construct global information completion models. In this article, beyond the image-tag relation, we take full advantage of the ubiquitous GPS locations and image-user relationship to enhance the accuracy of tag prediction and improve the computational efficiency. For GPS locations, we define the popular geo-locations where people tend to take more images as Points of Interests (POI), which are discovered by mean shift approach. For image-user relationship, we integrate a localized prior constraint, expecting the completed tag sub-matrix in each POI to maintain consistency with users' tagging behaviors. Based on these two key issues, we propose a unified tag matrix completion framework, which learns the image-tag relation within each POI. To solve the optimization problem, an efficient proximal sub-gradient descent algorithm is designed. The model optimization can be easily parallelized and distributed to learn the tag sub-matrix for each POI. Extensive experimental results reveal that the learned tag sub-matrix of each POI reflects the major trend of users' tagging results with respect to different POIs and users, and the parallel learning process provides strong support for processing large-scale online image databases. To fit the response time requirement and storage limitations of Tag-based Image Retrieval (TBIR) on mobile devices, we introduce Asymmetric Locality Sensitive Hashing (ALSH) to reduce the time cost and meanwhile improve the efficiency of retrieval.
资助项目National Basic Research Program of China[2012CB316400] ; National Basic Research Program of China[2015CB351802] ; National Natural Science Foundation of China[61332016] ; National Natural Science Foundation of China[61303160] ; National Natural Science Foundation of China[61572488] ; National Natural Science Foundation of China[61620106009] ; National Natural Science Foundation of China[61672497] ; 863 program of China[2014AA015202] ; Postdoctoral Science Foundation of China[2014T70126] ; Basic Research Program of Shenzhen[JCYJ20140610152828686] ; Key Research Program of Frontier Sciences, CAS[QYZDJ-SSW-SYS013]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000400160800005
出版者ASSOC COMPUTING MACHINERY
源URL[http://119.78.100.204/handle/2XEOYT63/6943]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Zhang, Jiaming
作者单位1.Chinese Acad Sci, Key Lab Intellectual Informat Proc, Inst Comp Technol, Beijing, Peoples R China
2.Univ Chinese Acad Sci, Beijing, Peoples R China
推荐引用方式
GB/T 7714		 Zhang, Jiaming,Wang, Shuhui,Huang, Qingming. Location-Based Parallel Tag Completion for Geo-Tagged Social Image Retrieval[J]. ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY,2017,8(3):21.
APA Zhang, Jiaming,Wang, Shuhui,&Huang, Qingming.(2017).Location-Based Parallel Tag Completion for Geo-Tagged Social Image Retrieval.ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY,8(3),21.
MLA Zhang, Jiaming,et al."Location-Based Parallel Tag Completion for Geo-Tagged Social Image Retrieval".ACM TRANSACTIONS ON INTELLIGENT SYSTEMS AND TECHNOLOGY 8.3(2017):21.

入库方式： OAI收割

来源：计算技术研究所

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