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Distributed image understanding with semantic dictionary and semantic expansion
Li, Liang1; Yan, Chenggang Clarence2; Chen, Xing3,4; Zhang, Chunjie1; Yin, Jian6; Jiang, Baochen6; Huang, Qingming1,5
2016-01-22
Source PublicationNEUROCOMPUTING
ISSN0925-2312
Volume174Pages:384-392
AbstractWeb-scale image understanding is drawing more and more attention from the computer vision and multimedia domain. To solve the key problem of visual polysemia and concept polymorphism in the image understanding, this paper proposes a semantic dictionary to describe the images on the level of semantic. The semantic dictionary characterizes the probability distribution between visual appearances and semantic concepts, and the learning procedure of semantic dictionary is formulated into a minimization optimization problem. Mixed-norm regularization is adopted to solve the above optimization for learning the concept membership distribution of visual appearance. Furthermore, to improve the generalization ability of the semantic description, we propose the semantic expansion technology, where a concept transferring matrix is learnt to quantize the implicit relevancy among the concepts. Finally, the distributed framework on the basis of the semantic dictionary is constructed to speed up the large scale image understanding. The semantic dictionary is validated in the tasks of large scale semantic image search and image annotation. (C) 2015 Elsevier B.V. All rights reserved.
KeywordImage understanding Semantic dictionary Multi-task learning Semantic expansion Distributed systems
DOI10.1016/j.neucom.2015.04.108
Language英语
Funding ProjectNational Basic Research Program of China (973 Program)[2012CB316400] ; National Basic Research Program of China (973 Program)[2015CB351802] ; National Natural Science Foundation of China[61332016] ; National Natural Science Foundation of China[61025011] ; National Natural Science Foundation of China[61402431] ; National Natural Science Foundation of China[61303154] ; National Natural Science Foundation of China[11301517] ; National Natural Science Foundation of China[61472203] ; China Postdoctoral Science Foundation
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence
WOS IDWOS:000367276700038
PublisherELSEVIER SCIENCE BV
Citation statistics
Cited Times:12[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/21672
Collection中国科学院数学与系统科学研究院
Affiliation1.Univ Chinese Acad Sci, Key Lab Big Data Min & Knowledge Management, Beijing, Peoples R China
2.Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China
3.Chinese Acad Sci, Acad Math & Syst Sci, Beijing, Peoples R China
4.Chinese Acad Sci, Natl Ctr Math & Interdisciplinary Sci, Beijing, Peoples R China
5.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing, Peoples R China
6.Shandong Univ, Dept Comp, Weihai, Peoples R China
Recommended Citation
GB/T 7714
Li, Liang,Yan, Chenggang Clarence,Chen, Xing,et al. Distributed image understanding with semantic dictionary and semantic expansion[J]. NEUROCOMPUTING,2016,174:384-392.
APA Li, Liang.,Yan, Chenggang Clarence.,Chen, Xing.,Zhang, Chunjie.,Yin, Jian.,...&Huang, Qingming.(2016).Distributed image understanding with semantic dictionary and semantic expansion.NEUROCOMPUTING,174,384-392.
MLA Li, Liang,et al."Distributed image understanding with semantic dictionary and semantic expansion".NEUROCOMPUTING 174(2016):384-392.
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