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VFM: Visual Feedback Model for Robust Object Recognition
Wang, Chong; Huang, Kai-Qi
2015-03-01
发表期刊JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
卷号30期号:2页码:325-339
文章类型Article
摘要Object recognition, which consists of classification and detection, has two important attributes for robustness: 1) closeness: detection windows should be as close to object locations as possible, and 2) adaptiveness: object matching should be adaptive to object variations within an object class. It is difficult to satisfy both attributes using traditional methods which consider classification and detection separately; thus recent studies propose to combine them based on confidence contextualization and foreground modeling. However, these combinations neglect feature saliency and object structure, and biological evidence suggests that the feature saliency and object structure can be important in guiding the recognition from low level to high level. In fact, object recognition originates in the mechanism of "what" and "where" pathways in human visual systems. More importantly, these pathways have feedback to each other and exchange useful information, which may improve closeness and adaptiveness. Inspired by the visual feedback, we propose a robust object recognition framework by designing a computational visual feedback model (VFM) between classification and detection. In the "what" feedback, the feature saliency from classification is exploited to rectify detection windows for better closeness; while in the "where" feedback, object parts from detection are used to match object structure for better adaptiveness. Experimental results show that the "what" and "where" feedback is effective to improve closeness and adaptiveness for object recognition, and encouraging improvements are obtained on the challenging PASCAL VOC 2007 dataset.
关键词Object Recognition Object Classification Object Detection Visual Feedback
WOS标题词Science & Technology ; Technology
关键词[WOS]IMAGE CLASSIFICATION ; POSE ESTIMATION ; ATTENTION ; VISION ; LOCALIZATION ; ALTERNATIVES ; ENHANCEMENT ; HISTOGRAMS ; COMPONENTS ; NETWORKS
收录类别SCI
语种英语
WOS研究方向Computer Science
WOS类目Computer Science, Hardware & Architecture ; Computer Science, Software Engineering
WOS记录号WOS:000351292400011
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被引频次:4[WOS]   [WOS记录]     [WOS相关记录]
文献类型期刊论文
条目标识符http://ir.ia.ac.cn/handle/173211/8090
专题智能感知与计算研究中心
作者单位Chinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
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Wang, Chong,Huang, Kai-Qi. VFM: Visual Feedback Model for Robust Object Recognition[J]. JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,2015,30(2):325-339.
APA Wang, Chong,&Huang, Kai-Qi.(2015).VFM: Visual Feedback Model for Robust Object Recognition.JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY,30(2),325-339.
MLA Wang, Chong,et al."VFM: Visual Feedback Model for Robust Object Recognition".JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY 30.2(2015):325-339.
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