Knowledge Commons of Institute of Automation，CAS
| Biologically Inspired Model for Visual Cognition Achieving Unsupervised Episodic and Semantic Feature Learning | |
Qiao, Hong1 ; Li, Yinlin1; Li, Fengfu2; Xi, Xuanyang1; Wu, Wei1
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| 发表期刊 | IEEE TRANSACTIONS ON CYBERNETICS
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| 2016-10-01 | |
| 卷号 | 46期号:10页码:2335-2347 |
| 文章类型 | Article |
| 摘要 | Recently, many biologically inspired visual computational models have been proposed. The design of these models follows the related biological mechanisms and structures, and these models provide new solutions for visual recognition tasks. In this paper, based on the recent biological evidence, we propose a framework to mimic the active and dynamic learning and recognition process of the primate visual cortex. From principle point of view, the main contributions are that the framework can achieve unsupervised learning of episodic features (including key components and their spatial relations) and semantic features (semantic descriptions of the key components), which support higher level cognition of an object. From performance point of view, the advantages of the framework are as follows: 1) learning episodic features without supervision-for a class of objects without a prior knowledge, the key components, their spatial relations and cover regions can be learned automatically through a deep neural network (DNN); 2) learning semantic features based on episodic features-within the cover regions of the key components, the semantic geometrical values of these components can be computed based on contour detection; 3) forming the general knowledge of a class of objects-the general knowledge of a class of objects can be formed, mainly including the key components, their spatial relations and average semantic values, which is a concise description of the class; and 4) achieving higher level cognition and dynamic updating-for a test image, the model can achieve classification and subclass semantic descriptions. And the test samples with high confidence are selected to dynamically update the whole model. Experiments are conducted on face images, and a good performance is achieved in each layer of the DNN and the semantic description learning process. Furthermore, the model can be generalized to recognition tasks of other objects with learning ability. |
| 关键词 | Biologically Inspired Hierarchical Model Key Components Learning Semantic Description |
| WOS标题词 | Science & Technology ; Technology |
| 学科领域 | 模式识别与智能系统 |
| DOI | 10.1109/TCYB.2015.2476706 |
| 关键词[WOS] | EXTERNAL FEATURES ; OBJECT RECOGNITION ; UNFAMILIAR FACES ; PERCEPTION ; CORTEX ; IDENTIFICATION ; PROSOPAGNOSIA ; ADAPTATION ; MECHANISMS ; KNOWLEDGE |
| 收录类别 | SCI ; SSCI |
| 语种 | 英语 |
| 项目资助者 | National Natural Science Foundation of China(61210009) |
| WOS研究方向 | Computer Science |
| WOS类目 | Computer Science, Artificial Intelligence ; Computer Science, Cybernetics |
| WOS记录号 | WOS:000384265600004 |
| 引用统计 | |
| 文献类型 | 期刊论文 |
| 条目标识符 | http://ir.ia.ac.cn/handle/173211/11640 |
| 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, State Key Lab Management & Control Complex Syst, Inst Automat, Beijing 100190, Peoples R China 2.Chinese Acad Sci, Acad Math & Syst Sci, Inst Appl Math, Beijing 100190, Peoples R China |
| 第一作者单位 | 中国科学院自动化研究所 |
| 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 | Qiao, Hong,Li, Yinlin,Li, Fengfu,et al. Biologically Inspired Model for Visual Cognition Achieving Unsupervised Episodic and Semantic Feature Learning[J]. IEEE TRANSACTIONS ON CYBERNETICS,2016,46(10):2335-2347. |
| APA | Qiao, Hong,Li, Yinlin,Li, Fengfu,Xi, Xuanyang,&Wu, Wei.(2016).Biologically Inspired Model for Visual Cognition Achieving Unsupervised Episodic and Semantic Feature Learning.IEEE TRANSACTIONS ON CYBERNETICS,46(10),2335-2347. |
| MLA | Qiao, Hong,et al."Biologically Inspired Model for Visual Cognition Achieving Unsupervised Episodic and Semantic Feature Learning".IEEE TRANSACTIONS ON CYBERNETICS 46.10(2016):2335-2347. |
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| tp3.pdf（2781KB） | 期刊论文 | 作者接受稿 | 开放获取 | CC BY-NC-SA | 浏览 下载 | |
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