Object Categorization Using Class-Specific Representations

doi:10.1109/TNNLS.2017.2757497

	Object Categorization Using Class-Specific Representations
	Zhang, Chunjie 1,2; Cheng, Jian 2,3,4; Li, Liang 5; Li, Changsheng 6; Tian, Qi 7
	2018-09-01
发表期刊	IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS
ISSN	2162-237X
卷号	29 期号:9 页码:4528-4534
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
DOI	10.1109/TNNLS.2017.2757497
收录类别	SCI
语种	英语
资助项目	National Natural Science Foundation of China[61303154] ; National Natural Science Foundation of China[61332016] ; 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
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
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计	被引频次：9[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/5004
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Cheng, Jian
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
推荐引用方式 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.