CASIA OpenIR
Cross-Modality Bridging and Knowledge Transferring for Image Understanding
Yan, Chenggang1; Li, Liang2,3; Zhang, Chunjie4; Liu, Bingtao1; Zhang, Yongdong5; Dai, Qionghai6
Source PublicationIEEE TRANSACTIONS ON MULTIMEDIA
ISSN1520-9210
2019-10-01
Volume21Issue:10Pages:2675-2685
Corresponding AuthorLi, Liang(liang.li@ict.ac.cn)
AbstractThe understanding of web images has been a hot research topic in both artificial intelligence and multimedia content analysis domains. The web images are composed of various complex foregrounds and backgrounds, which makes the design of an accurate and robust learning algorithm a challenging task. To solve the above significant problem, first, we learn a cross-modality bridging dictionary for the deep and complete understanding of a vast quantity of web images. The proposed algorithm leverages the visual features into the semantic concept probability distribution, which can construct a global semantic description for images while preserving the local geometric structure. To discover and model the occurrence patterns between intra- and inter-categories, multi-task learning is introduced for formulating the objective formulation with Capped-l(1) penalty, which can obtain the optimal solution with a higher probability and outperform the traditional convex function-based methods. Second, we propose a knowledge-based concept transferring algorithm to discover the underlying relations of different categories. This distribution probability transferring among categories can bring the more robust global feature representation, and enable the image semantic representation to generalize better as the scenario becomes larger. Experimental comparisons and performance discussion with classical methods on the ImageNet, Caltech-256, SUN397, and Scene15 datasets show the effectiveness of our proposed method at three traditional image understanding tasks.
KeywordObject and scene recognition image semantic search cross-modality bridging multi-task learning knowledge transferring
DOI10.1109/TMM.2019.2903448
WOS KeywordRETRIEVAL ; CLASSIFICATION ; OBJECT
Indexed BySCI
Language英语
Funding ProjectNational Basic Research Program of China (973-Program)[2015CB351802] ; National Natural Science Foundation of China[61771457] ; National Natural Science Foundation of China[61732007] ; National Natural Science Foundation of China[61572488] ; National Natural Science Foundation of China[61472389] ; National Natural Science Foundation of China[61872362] ; National Natural Science Foundation of China[U163621] ; National Natural Science Foundation of China[61671196] ; National Natural Science Foundation of China[61525206] ; National Natural Science Foundation of China[61672497] ; Key Research Program of Frontier Sciences, CAS[QYZDJ-SSW-SYS013] ; Zhejiang Province Nature Science Foundation of China[LR17F030006] ; National Key Research and Development Program of China[2017YFC0820600]
Funding OrganizationNational Basic Research Program of China (973-Program) ; National Natural Science Foundation of China ; Key Research Program of Frontier Sciences, CAS ; Zhejiang Province Nature Science Foundation of China ; National Key Research and Development Program of China
WOS Research AreaComputer Science ; Telecommunications
WOS SubjectComputer Science, Information Systems ; Computer Science, Software Engineering ; Telecommunications
WOS IDWOS:000489728400020
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Cited Times:46[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/26595
Collection中国科学院自动化研究所
Corresponding AuthorLi, Liang
Affiliation1.Hangzhou Dianzi Univ, Inst Informat & Control, Hangzhou 310005, Zhejiang, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
3.Univ Chinese Acad Sci, Coll Comp & Control Engn, Beijing 100190, Peoples R China
4.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
5.Chinese Acad Sci, Inst Comp Technol, Adv Comp Res Lab, Beijing 100190, Peoples R China
6.Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China
Recommended Citation
GB/T 7714
Yan, Chenggang,Li, Liang,Zhang, Chunjie,et al. Cross-Modality Bridging and Knowledge Transferring for Image Understanding[J]. IEEE TRANSACTIONS ON MULTIMEDIA,2019,21(10):2675-2685.
APA Yan, Chenggang,Li, Liang,Zhang, Chunjie,Liu, Bingtao,Zhang, Yongdong,&Dai, Qionghai.(2019).Cross-Modality Bridging and Knowledge Transferring for Image Understanding.IEEE TRANSACTIONS ON MULTIMEDIA,21(10),2675-2685.
MLA Yan, Chenggang,et al."Cross-Modality Bridging and Knowledge Transferring for Image Understanding".IEEE TRANSACTIONS ON MULTIMEDIA 21.10(2019):2675-2685.
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