Enhanced Biologically Inspired Model for Object Recognition
文献类型：期刊论文
| 作者 | Huang, Yongzhen1 ; Huang, Kaiqi1; Tao, Dacheng2; Tan, Tieniu1; Li, Xuelong3
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| 刊名 | IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
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| 出版日期 | 2011-12-01 |
| 卷号 | 41期号:6页码:1668-1680 |
| 关键词 | Biologically inspired model (BIM) feedback object recognition sparseness |
| 英文摘要 | 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. |
| WOS标题词 | Science & Technology ; Technology |
| 类目[WOS] | Automation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics |
| 研究领域[WOS] | Automation & Control Systems ; Computer Science |
| 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记录号 | WOS:000297342100018 |
| 源URL | [http://ir.ia.ac.cn/handle/173211/3771]  |
| 专题 | 自动化研究所_智能感知与计算研究中心 |
| 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 |
| 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. |
入库方式： OAI收割
来源：自动化研究所
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