Robust Object Recognition via Visual Pathway Feedback

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	Robust Object Recognition via Visual Pathway Feedback
	Chong Wang; Junge Zhang; Peipei Yang; Kaiqi Huang
	2014
会议名称	International Conference on Pattern Recognition (ICPR)
会议录名称	ICPR
会议日期	2014
会议地点	Stockholm,Sweden
摘要	Object recognition, which consists of classification and detection, has two important attributes for robustness: (1) Closeness: detection windows should be close to object locations, and (2) Adaptiveness: object matching should be adaptive to object variations in classification. It is difficult to satisfy both attributes by considering classification and detection separately, thus recent studies combine them based on confidence contextualization and foreground modeling. However, these combinations neglect feature saliency and object structure, which are important for recognition. In fact, object recognition originates in the mechanism of “what” and “where” pathways in human visual systems, and more importantly, these pathways have feedback to each other, which provides a probable way to improve closeness and adaptiveness. Inspired by the feedback, we propose a robust object recognition framework by designing a computational model of the feedback mechanism. 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 model object matching of object structure for better adaptiveness. Experiments show that the “what” and “where” feedback can be effective to improve closeness and adaptiveness for robust object recognition, and encouraging results are obtained on the challenging PASCAL VOC 2007 dataset
关键词	Object Recognition Bag Of Words Visual Pathway Feedback
文献类型	会议论文
条目标识符	http://ir.ia.ac.cn/handle/173211/12430
专题	智能感知与计算研究中心
通讯作者	Kaiqi Huang
作者单位	CASIA
第一作者单位	中国科学院自动化研究所
通讯作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Chong Wang,Junge Zhang,Peipei Yang,et al. Robust Object Recognition via Visual Pathway Feedback[C],2014.

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