| Enhanced Biologically Inspired Model for Object Recognition | |
| Huang, Yongzhen1; Huang, Kaiqi1; Tao, Dacheng2; Tan, Tieniu1; Li, Xuelong3 | |
| 作者部门 | 光学影像分析与学习中心 |
| 2011-12-01 | |
| 发表期刊 | IEEE TRANSACTIONS ON SYSTEMS MAN AND CYBERNETICS PART B-CYBERNETICS
 |
| ISSN | 1083-4419 |
| 卷号 | 41期号:6页码:1668-1680 |
| 产权排序 | 2 |
| 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. |
| 文章类型 | Article |
| 关键词 | Biologically Inspired Model (Bim) Feedback Object Recognition Sparseness |
| 学科领域 | Automation & Control Systems |
| WOS标题词 | Science & Technology ; Technology |
| DOI | 10.1109/TSMCB.2011.2158418 |
| 收录类别 | SCI ; SSCi ; EI |
| 关键词[WOS] | VISUAL-SYSTEM ; CORTEX ; RETRIEVAL ; FEATURES ; SCALE ; CLASSIFICATION ; SURVEILLANCE ; HISTOGRAMS ; REGIONS ; SPEED |
| 语种 | 英语 |
| WOS研究方向 | Automation & Control Systems ; Computer Science |
| 项目资助者 | National Natural Science Foundation of China;NLPR;National Hi-Tech Research and Development Program of China;Tsinghua National Laboratory for Information Science and Technology;National Basic Research Program of China (973 Program);Open Project Foundation of State Key Laboratory of Industrial Control Technology |
| WOS类目 | Automation & Control Systems ; Computer Science, Artificial Intelligence ; Computer Science, Cybernetics |
| WOS记录号 | WOS:000297342100018 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.opt.ac.cn/handle/181661/19864 |
| 专题 | 光谱成像技术研究室 |
| 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 |
| 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. |
| 条目包含的文件 | ||||||
| 文件名称/大小 | 文献类型 | 版本类型 | 开放类型 | 使用许可 | ||
| Enhanced Biologicall（1845KB） | 期刊论文 | 出版稿 | 限制开放 | CC BY-NC-SA | 请求全文 | |
除非特别说明，本系统中所有内容都受版权保护，并保留所有权利。
修改评论