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Object Categorization Using Class-Specific Representations
Zhang, Chunjie1,2; Cheng, Jian2,3,4; Li, Liang5; Li, Changsheng6; Tian, Qi7
2018-09-01
发表期刊IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
卷号29期号:9页码:4528-4534
文章类型Article
摘要Object categorization refers to the task of automatically classifying objects based on the visual content. Existing approaches simply represent each image with the visual features without considering the specific characters of images within the same class. However, objects of the same class may exhibit unique characters, which should be represented accordingly. In this brief, we propose a novel class-specific representation strategy for object categorization. For each class, we first model the characters of images within the same class using Gaussian mixture model (GMM). We then represent each image by calculating the Euclidean distance and relative Euclidean distance between the image and the GMM model for each class. We concatenate the representations of each class for joint representation. In this way, we can represent an image by not only considering the visual contents but also combining the class-specific characters. Experiments on several public available data sets validate the superiority of the proposed class-specific representation method over well-established algorithms for object category predictions.
关键词Class-specific Representation Image Classification Object Categorization Visual Representation
WOS标题词Science & Technology ; Technology
DOI10.1109/TNNLS.2017.2757497
关键词[WOS]IMAGE CLASSIFICATION ; LOW-RANK ; DICTIONARY ; CODEBOOKS ; KERNEL ; SPACE
收录类别SCI
语种英语
项目资助者National Natural Science Foundation of China(61303154 ; Scientific Research Key Program of Beijing Municipal Commission of Education(KZ201610005012) ; ARO(W911NF-15-1-0290) ; NEC Laboratory of America ; National Science Foundation of China(61429201) ; NEC Laboratory of Blippar ; 61332016)
WOS研究方向Computer Science ; Engineering
WOS类目Computer Science, Artificial Intelligence ; Computer Science, Hardware & Architecture ; Computer Science, Theory & Methods ; Engineering, Electrical & Electronic
WOS记录号WOS:000443083700052
引用统计
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/15479
专题类脑智能研究中心
作者单位1.Chinese Acad Sci, Inst Automat, Res Ctr Brain Inspired Intelligence, Beijing 100190, Peoples R China
2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China
3.Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100049, Peoples R China
4.Chinese Acad Sci, Ctr Excellence Brain Sci & Intelligence Technol, Beijing 100049, Peoples R China
5.Chinese Acad Sci, Inst Comp Technol, Key Lab Intelligent Informat Proc, Beijing 100049, Peoples R China
6.Univ Elect Sci & Technol China, Sch Comp Sci & Engn, Chengdu 611731, Sichuan, Peoples R China
7.Univ Texas San Antonio, Dept Comp Sci, San Antonio, TX 78249 USA
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GB/T 7714
Zhang, Chunjie,Cheng, Jian,Li, Liang,et al. Object Categorization Using Class-Specific Representations[J]. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,2018,29(9):4528-4534.
APA Zhang, Chunjie,Cheng, Jian,Li, Liang,Li, Changsheng,&Tian, Qi.(2018).Object Categorization Using Class-Specific Representations.IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS,29(9),4528-4534.
MLA Zhang, Chunjie,et al."Object Categorization Using Class-Specific Representations".IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 29.9(2018):4528-4534.
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