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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
2018-06-01
Source PublicationIEEE TRANSACTIONS ON COGNITIVE AND DEVELOPMENTAL SYSTEMS
ISSN2379-8920
Volume10Issue:2Pages:420-431
AbstractTechniques 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.
KeywordBiologically inspired model object recognition semantic learning structural learning
DOI10.1109/TCDS.2017.2749978
Language英语
Funding ProjectNational 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]
WOS Research AreaComputer Science ; Robotics ; Neurosciences & Neurology
WOS SubjectComputer Science, Artificial Intelligence ; Robotics ; Neurosciences
WOS IDWOS:000435198600025
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/30521
Collection应用数学研究所
Affiliation1.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
Recommended Citation
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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