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Cross-Modality Bridging and Knowledge Transferring for Image Understanding

文献类型：期刊论文

作者Yan, Chenggang3; Li, Liang4,5; Zhang, Chunjie6; Liu, Bingtao3; Zhang, Yongdong2; Dai, Qionghai1
刊名IEEE TRANSACTIONS ON MULTIMEDIA
出版日期2019-10-01
卷号21期号:10页码:2675-2685
ISSN号1520-9210
关键词Object and scene recognition image semantic search cross-modality bridging multi-task learning knowledge transferring
DOI10.1109/TMM.2019.2903448
英文摘要The 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.
资助项目National 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]
WOS研究方向Computer Science ; Telecommunications
语种英语
出版者IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
WOS记录号WOS:000489728400020
源URL[http://119.78.100.204/handle/2XEOYT63/4605]  
专题中国科学院计算技术研究所期刊论文_英文
通讯作者Li, Liang
作者单位1.Tsinghua Univ, Dept Automat, Beijing 100084, Peoples R China
2.Chinese Acad Sci, Inst Comp Technol, Adv Comp Res Lab, Beijing 100190, Peoples R China
3.Hangzhou Dianzi Univ, Inst Informat & Control, Hangzhou 310005, Zhejiang, Peoples R China
4.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100190, Peoples R China
5.Univ Chinese Acad Sci, Coll Comp & Control Engn, Beijing 100190, Peoples R China
6.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式
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.

入库方式： OAI收割

来源：计算技术研究所

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