VFM: Visual Feedback Model for Robust Object Recognition

CASIA OpenIR > 智能感知与计算研究中心

	VFM: Visual Feedback Model for Robust Object Recognition
	Wang, Chong; Huang, Kai-Qi
发表期刊	JOURNAL OF COMPUTER SCIENCE AND TECHNOLOGY
	2015-03-01
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
引用统计	被引频次：6[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
第一作者单位	模式识别国家重点实验室
推荐引用方式 GB/T 7714	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.

条目包含的文件		下载所有文件
文件名称/大小	文献类型	版本类型	开放类型	使用许可
Chong Wang_VFM Visua（24239KB）	期刊论文	作者接受稿	开放获取	CC BY-NC-SA	浏览下载