Enhanced Biologically Inspired Model for Object Recognition

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	Enhanced Biologically Inspired Model for Object Recognition
	Huang, Yongzhen1 ; Huang, Kaiqi1 ; Tao, Dacheng 2; Tan, Tieniu1 ; Li, Xuelong 3
发表期刊	IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
	2011-12-01
卷号	41 期号:6 页码:1668-1680
文章类型	Article
摘要	The biologically inspired model (BIM) proposed by Serre et al. presents a promising solution to object categorization. It emulates the process of object recognition in primates' visual cortex by constructing a set of scale- and position-tolerant features whose properties are similar to those of the cells along the ventral stream of visual cortex. However, BIM has potential to be further improved in two aspects: mismatch by dense input and randomly feature selection due to the feedforward framework. To solve or alleviate these limitations, we develop an enhanced BIM (EBIM) in terms of the following two aspects: 1) removing uninformative inputs by imposing sparsity constraints, 2) apply a feedback loop to middle level feature selection. Each aspect is motivated by relevant psychophysical research findings. To show the effectiveness of the EBIM, we apply it to object categorization and conduct empirical studies on four computer vision data sets. Experimental results demonstrate that the EBIM outperforms the BIM and is comparable to state-of-the-art approaches in terms of accuracy. Moreover, the new system is about 20 times faster than the BIM.
关键词	Biologically Inspired Model (Bim) Feedback Object Recognition Sparseness
WOS标题词	Science & Technology ; Technology
关键词[WOS]	VISUAL-SYSTEM ; CORTEX ; RETRIEVAL ; FEATURES ; SCALE ; CLASSIFICATION ; SURVEILLANCE ; HISTOGRAMS ; REGIONS ; SPEED
收录类别	SCI ; SSCI
语种	英语
WOS研究方向	Automation & Control Systems ; Computer Science
WOS类目	Automation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS记录号	WOS:000297342100018
引用统计	被引频次：61[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/173211/3771
专题	模式识别实验室
作者单位	1.Chinese Acad Sci, Natl Lab Pattern Recognit, Inst Automat, Beijing 100190, Peoples R China 2.Univ Technol Sydney, Ctr Quantum Computat & Informat Syst, Sydney, NSW 2007, Australia 3.Chinese Acad Sci, Ctr Opt IMagery Anal & Learning OPTIMAL, State Key Lab Transient Opt & Photon, Xian Inst Opt & Precis Mech, Xian 710119, 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	Huang, Yongzhen,Huang, Kaiqi,Tao, Dacheng,et al. Enhanced Biologically Inspired Model for Object Recognition[J]. IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS,2011,41(6):1668-1680.
APA	Huang, Yongzhen,Huang, Kaiqi,Tao, Dacheng,Tan, Tieniu,&Li, Xuelong.(2011).Enhanced Biologically Inspired Model for Object Recognition.IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS,41(6),1668-1680.
MLA	Huang, Yongzhen,et al."Enhanced Biologically Inspired Model for Object Recognition".IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS 41.6(2011):1668-1680.