CASIA OpenIR  > 智能感知与计算研究中心
Enhanced Biologically Inspired Model for Object Recognition
Huang, Yongzhen1; Huang, Kaiqi1; Tao, Dacheng2; Tan, Tieniu1; Li, Xuelong3
Source PublicationIEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
2011-12-01
Volume41Issue:6Pages:1668-1680
SubtypeArticle
AbstractThe 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.
KeywordBiologically Inspired Model (Bim) Feedback Object Recognition Sparseness
WOS HeadingsScience & Technology ; Technology
WOS KeywordVISUAL-SYSTEM ; CORTEX ; RETRIEVAL ; FEATURES ; SCALE ; CLASSIFICATION ; SURVEILLANCE ; HISTOGRAMS ; REGIONS ; SPEED
Indexed BySCI ; SSCI
Language英语
WOS Research AreaAutomation & Control Systems ; Computer Science
WOS SubjectAutomation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000297342100018
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Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/3771
Collection智能感知与计算研究中心
Affiliation1.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
First Author AffilicationChinese Acad Sci, Inst Automat, Natl Lab Pattern Recognit, Beijing 100190, Peoples R China
Recommended Citation
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.
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