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Online selection of the best k-feature subset for object tracking

文献类型：期刊论文

作者Li, Guorong2; Huang, Qingming1,2; Pang, Junbiao1; Jiang, Shuqiang1; Qin, Lei1
刊名JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION
出版日期2012-02-01
卷号23期号:2页码:254-263
关键词Object tracking Feature subset selection Feature selection Feature subset tree Conditional entropy Greedy search algorithm Particle filter Online selection
ISSN号1047-3203
DOI10.1016/j.jvcir.2011.11.001
英文摘要In this paper, we propose a new feature subset evaluation method for feature selection in object tracking. According to the fact that a feature which is useless by itself could become a good one when it is used together with some other features, we propose to evaluate feature subsets as a whole for object tracking instead of scoring each feature individually and find out the most distinguishable subset for tracking. In the paper, we use a special tree to formalize the feature subset space. Then conditional entropy is used to evaluating feature subset and a simple but efficient greedy search algorithm is developed to search this tree to obtain the optimal k-feature subset quickly. Furthermore, our online k-feature subset selection method is integrated into particle filter for robust tracking. Extensive experiments demonstrate that k-feature subset selected by our method is more discriminative and thus can improve tracking performance considerably. (C) 2011 Elsevier Inc. All rights reserved.
资助项目National Basic Research Program of China (973 Program)[2009CB320906] ; National Natural Science Foundation of China[61025011] ; National Natural Science Foundation of China[61035001] ; National Natural Science Foundation of China[61133003] ; National Natural Science Foundation of China[61003165] ; Beijing Natural Science Foundation[4111003]
WOS研究方向Computer Science
语种英语
WOS记录号WOS:000300198900003
出版者ACADEMIC PRESS INC ELSEVIER SCIENCE
源URL[http://119.78.100.204/handle/2XEOYT63/5379]  
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, Guorong
作者单位1.CAS, Inst Comput Tech, Key Lab Intell Info Proc, Beijing 100080, Peoples R China
2.Chinese Acad Sci, Grad Univ, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Li, Guorong,Huang, Qingming,Pang, Junbiao,et al. Online selection of the best k-feature subset for object tracking[J]. JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION,2012,23(2):254-263.
APA Li, Guorong,Huang, Qingming,Pang, Junbiao,Jiang, Shuqiang,&Qin, Lei.(2012).Online selection of the best k-feature subset for object tracking.JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION,23(2),254-263.
MLA Li, Guorong,et al."Online selection of the best k-feature subset for object tracking".JOURNAL OF VISUAL COMMUNICATION AND IMAGE REPRESENTATION 23.2(2012):254-263.

入库方式： OAI收割

来源：计算技术研究所

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