Knowledge Commons of Institute of Automation，CAS
| A Novel Biologically Inspired Visual Cognition Model: Automatic Extraction of Semantics, Formation of Integrated Concepts, and Reselection Features for Ambiguity | |
Yin, Peijie1,2; Qiao, Hong2,3,4 ; Wu, Wei3,5; Qi, Lu3; Li, Yinlin3; Zhong, Shanlin2,3; Zhang, Bo1,2,6
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| 发表期刊 | IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS
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| ISSN | 2379-8920 |
| 2018-06-01 | |
| 卷号 | 10期号:2页码:420-431 |
| 通讯作者 | Qiao, Hong(hong.qiao@ia.ac.cn) |
| 摘要 | Techniques that integrate neuroscience and information science benefit both fields. Many related models have been proposed in computer vision; however, in general, the robustness and recognition precision are still key problems in object recognition models. In this paper, inspired by the process by which humans recognize objects and its biological mechanisms, a new integrated and dynamic framework is proposed that mimics the semantic extraction, concept formation and feature reselection found in human visual processing. The main contributions of the proposed model are as follows: 1) semantic feature extraction: local semantic features are learned from episodic features extracted from raw images using a deep neural network; 2) integrated concept formation: concepts are formed using the local semantic information and structural information is learned through a network; and 3) feature reselection: when ambiguity is detected during the recognition process, distinctive features based on the differences between the ambiguous candidates are reselected for recognition. Experimental results on four datasets show that-compared with other methods-the new proposed model is more robust and achieves higher precision for visual recognition, especially when the input samples are semantically ambiguous. Meanwhile, the introduced biological mechanisms further strengthen the interaction between neuroscience and information science. |
| 关键词 | Biologically inspired model object recognition semantic learning structural learning |
| DOI | 10.1109/TCDS.2017.2749978 |
| 关键词[WOS] | NEURAL MECHANISMS ; HUMAN BRAIN ; AREA V4 ; ATTENTION ; MEMORY ; RECOGNITION ; REPRESENTATION ; KNOWLEDGE ; SHAPE ; SYSTEM |
| 收录类别 | SCI |
| 语种 | 英语 |
| 资助项目 | National Science Foundation of China[61210009] ; Strategic Priority Research Program of the CAS[XDB02080003] ; National Key Research and Development Plan of China[2016YFC0300801] ; National Natural Science Foundation of China[61210009] ; National Natural Science Foundation of China[61627808] ; National Natural Science Foundation of China[91648205] ; Development of Science and Technology of Guangdong Province Special Fund Project[2016B090910001] ; National Science Foundation of China[61210009] ; Strategic Priority Research Program of the CAS[XDB02080003] ; National Key Research and Development Plan of China[2016YFC0300801] ; National Natural Science Foundation of China[61210009] ; National Natural Science Foundation of China[61627808] ; National Natural Science Foundation of China[91648205] ; Development of Science and Technology of Guangdong Province Special Fund Project[2016B090910001] |
| 项目资助者 | National Science Foundation of China ; Strategic Priority Research Program of the CAS ; National Key Research and Development Plan of China ; National Natural Science Foundation of China ; Development of Science and Technology of Guangdong Province Special Fund Project |
| WOS研究方向 | Computer Science ; Robotics ; Neurosciences & Neurology |
| WOS类目 | Computer Science, Artificial Intelligence ; Robotics ; Neurosciences |
| WOS记录号 | WOS:000435198600025 |
| 出版者 | IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.ia.ac.cn/handle/173211/21731 |
| 专题 | 多模态人工智能系统全国重点实验室_机器人理论与应用 |
| 通讯作者 | Qiao, Hong |
| 作者单位 | 1.Chinese Acad Sci, Acad Math & Syst Sci, Inst Appl Math, Beijing 100190, Peoples R China 2.Univ Chinese Acad Sci, Beijing 100049, Peoples R China 3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China 4.CAS Ctr Excellence Brain Sci & Intelligence Techn, Shanghai 200031, Peoples R China 5.Beijing Key Lab Res & Applicat Robot Intelligence, Beijing 100190, Peoples R China 6.Chinese Acad Sci, Cloud Comp Ctr, Dongguan 523808, Peoples R China |
| 通讯作者单位 | 中国科学院自动化研究所 |
| 推荐引用方式 GB/T 7714 | Yin, Peijie,Qiao, Hong,Wu, Wei,et al. A Novel Biologically Inspired Visual Cognition Model: Automatic Extraction of Semantics, Formation of Integrated Concepts, and Reselection Features for Ambiguity[J]. IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS,2018,10(2):420-431. |
| APA | Yin, Peijie.,Qiao, Hong.,Wu, Wei.,Qi, Lu.,Li, Yinlin.,...&Zhang, Bo.(2018).A Novel Biologically Inspired Visual Cognition Model: Automatic Extraction of Semantics, Formation of Integrated Concepts, and Reselection Features for Ambiguity.IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS,10(2),420-431. |
| MLA | Yin, Peijie,et al."A Novel Biologically Inspired Visual Cognition Model: Automatic Extraction of Semantics, Formation of Integrated Concepts, and Reselection Features for Ambiguity".IEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS 10.2(2018):420-431. |
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