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Image-level classification by hierarchical structure learning with visual and semantic similarities

文献类型：期刊论文

作者Zhang, Chunjie1,4; Cheng, Jian2,3,4; Tian, Qi5
刊名INFORMATION SCIENCES
出版日期2018
卷号422期号:422页码:271-281
关键词Image Classification Hierarchical Structure Learning Image-level Modeling Object Categorization
DOI10.1016/j.ins.2017.09.024
文献子类Article
英文摘要Image classification methods often use class-level information without considering the distinctive character of each image. Images of the same class may have varied appearances. Besides, visually similar images may not be semantically correlated. To solve these problems, in this paper, we propose a novel image classification method by automatically learning the image-level hierarchical structure (ILHS) using both visual and semantic similarities. We try to generate new representations by exploring both visual and semantic similarities of images. Images are clustered hierarchically to explore their correlations. We then use them for image representations. The diversity of image classes within each cluster is used to re-weight visual similarities. The re-weighted similarities are aggregated to generate new image representations. We conduct image classification experiments on the Caltech-256 dataset, the PASCAL VOC 2007 dataset and the PASCAL VOC 2012 dataset. Experimental results demonstrate the effectiveness of the proposed method. (C) 2017 Elsevier Inc. All rights reserved.
WOS关键词LOW-RANK ; SPARSE DECOMPOSITION ; REPRESENTATION ; PREDICTION ; SPACE
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000414887900016
资助机构National Natural Science Foundation of China(61303154 ; Scientific Research Key Program of Beijing Municipal Commission of Education(KZ201610005012) ; ARO grant(W911NF-15-1-0290) ; NEC Laboratory of America ; NEC Laboratory of Blippar ; National Science Foundation of China (NSFC)(61429201) ; 61332016)
源URL[http://ir.ia.ac.cn/handle/173211/15316]  
专题类脑芯片与系统研究
作者单位1.Chinese Acad Sci, Inst Automat, Res Ctr Brain Inspired Intelligence, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing, Peoples R China
4.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
5.Univ Texas San Antonio, Dept Comp Sci, San Antonio, TX 78249 USA
推荐引用方式
GB/T 7714		 Zhang, Chunjie,Cheng, Jian,Tian, Qi. Image-level classification by hierarchical structure learning with visual and semantic similarities[J]. INFORMATION SCIENCES,2018,422(422):271-281.
APA Zhang, Chunjie,Cheng, Jian,&Tian, Qi.(2018).Image-level classification by hierarchical structure learning with visual and semantic similarities.INFORMATION SCIENCES,422(422),271-281.
MLA Zhang, Chunjie,et al."Image-level classification by hierarchical structure learning with visual and semantic similarities".INFORMATION SCIENCES 422.422(2018):271-281.

入库方式： OAI收割

来源：自动化研究所

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