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Biologically Inspired Visual Model With Preliminary Cognition and Active Attention Adjustment
Qiao, Hong1; Xi, Xuanyang2,3; Li, Yinlin2,3; Wu, Wei4; Li, Fengfu5
2015-11-01
Source PublicationIEEE TRANSACTIONS ON CYBERNETICS
ISSN2168-2267
Volume45Issue:11Pages:2612-2624
AbstractRecently, many computational models have been proposed to simulate visual cognition process. For example, the hierarchical Max-Pooling (HMAX) model was proposed according to the hierarchical and bottom-up structure of V1 to V4 in the ventral pathway of primate visual cortex, which could achieve position-and scale-tolerant recognition. In our previous work, we have introduced memory and association into the HMAX model to simulate visual cognition process. In this paper, we improve our theoretical framework by mimicking a more elaborate structure and function of the primate visual cortex. We will mainly focus on the new formation of memory and association in visual processing under different circumstances as well as preliminary cognition and active adjustment in the inferior temporal cortex, which are absent in the HMAX model. The main contributions of this paper are: 1) in the memory and association part, we apply deep convolutional neural networks to extract various episodic features of the objects since people use different features for object recognition. Moreover, to achieve a fast and robust recognition in the retrieval and association process, different types of features are stored in separated clusters and the feature binding of the same object is stimulated in a loop discharge manner and 2) in the preliminary cognition and active adjustment part, we introduce preliminary cognition to classify different types of objects since distinct neural circuits in a human brain are used for identification of various types of objects. Furthermore, active cognition adjustment of occlusion and orientation is implemented to the model to mimic the top-down effect in human cognition process. Finally, our model is evaluated on two face databases CAS-PEAL-R1 and AR. The results demonstrate that our model exhibits its efficiency on visual recognition process with much lower memory storage requirement and a better performance compared with the traditional purely computational methods.
KeywordActive attention adjustment association biologically inspired visual model memory object recognition
DOI10.1109/TCYB.2014.2377196
Language英语
Funding ProjectNational Natural Science Foundation of China[61033011] ; National Natural Science Foundation of China[61210009] ; National Key Technology Research and Development Program[2012BAI34B02]
WOS Research AreaComputer Science
WOS SubjectComputer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS IDWOS:000363233000020
PublisherIEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation statistics
Document Type期刊论文
Identifierhttp://ir.amss.ac.cn/handle/2S8OKBNM/21055
Collection中国科学院数学与系统科学研究院
Corresponding AuthorQiao, Hong
Affiliation1.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
2.Chinese Acad Sci, Grad Sch, Inst Automat, Beijing 100190, Peoples R China
3.Chinese Acad Sci, Inst Automat, State Key Lab Management & Control Complex Syst, Beijing 100190, Peoples R China
4.Univ Sci & Technol China, Hefei Natl Lab Phys Sci Microscale, Hefei 230027, Peoples R China
5.Chinese Acad Sci, Inst Appl Math, Acad Math & Syst Sci, Beijing 100190, Peoples R China
Recommended Citation
GB/T 7714
Qiao, Hong,Xi, Xuanyang,Li, Yinlin,et al. Biologically Inspired Visual Model With Preliminary Cognition and Active Attention Adjustment[J]. IEEE TRANSACTIONS ON CYBERNETICS,2015,45(11):2612-2624.
APA Qiao, Hong,Xi, Xuanyang,Li, Yinlin,Wu, Wei,&Li, Fengfu.(2015).Biologically Inspired Visual Model With Preliminary Cognition and Active Attention Adjustment.IEEE TRANSACTIONS ON CYBERNETICS,45(11),2612-2624.
MLA Qiao, Hong,et al."Biologically Inspired Visual Model With Preliminary Cognition and Active Attention Adjustment".IEEE TRANSACTIONS ON CYBERNETICS 45.11(2015):2612-2624.
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