A Novel Biologically Inspired Visual Cognition Model: Automatic Extraction of Semantics, Formation of Integrated Concepts, and Reselection Features for Ambiguity
Peijie Yin2; Hong Qiao1; Wei Wu1; Lu Qi1; Yinlin Li1; Shanlin Zhong1; Bo Zhang2
Source PublicationIEEE Transactions on Cognitive and Developmental Systems
2017-09
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
Subject Area模式识别与智能系统
DOI10.1109/TCDS.2017.2749978
Citation statistics
Cited Times:1[WOS]   [WOS Record]     [Related Records in WOS]
Document Type期刊论文
Identifierhttp://ir.ia.ac.cn/handle/173211/21731
Collection复杂系统管理与控制国家重点实验室_机器人理论与应用
Corresponding AuthorHong Qiao
Affiliation1.中科院自动化所复杂系统管理与控制国家重点实验室
2.中国科学院数学与系统科学研究院
Corresponding Author AffilicationInstitute of Automation, Chinese Academy of Sciences
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Peijie Yin,Hong Qiao,Wei Wu,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,2017,10(2):420-431.
APA Peijie Yin.,Hong Qiao.,Wei Wu.,Lu Qi.,Yinlin Li.,...&Bo Zhang.(2017).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 Peijie Yin,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(2017):420-431.
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