Biologically Inspired Visual Model With Preliminary Cognition and Active Attention Adjustment

doi:10.1109/TCYB.2014.2377196

CASIA OpenIR > 多模态人工智能系统全国重点实验室 > 机器人理论与应用

	Biologically Inspired Visual Model With Preliminary Cognition and Active Attention Adjustment
	Qiao, Hong1 ; Xi, Xuanyang2,3 ; Li, Yinlin2,3 ; Wu, Wei4 ; Li, Fengfu 5
发表期刊	IEEE TRANSACTIONS ON CYBERNETICS
	2015-11-01
卷号	45 期号:11 页码:2612-2624
文章类型	Article
摘要	Recently, 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.
关键词	Active Attention Adjustment Association Biologically Inspired Visual Model Memory Object Recognition
WOS标题词	Science & Technology ; Technology
DOI	10.1109/TCYB.2014.2377196
关键词[WOS]	OBJECT RECOGNITION ; AREA V4 ; INFEROTEMPORAL CORTEX ; FACE RECOGNITION ; SINGLE NEURONS ; CORTICAL AREAS ; INVARIANCE ; FEATURES ; SALIENCY ; MACAQUE
收录类别	SCI
语种	英语
项目资助者	National Natural Science Foundation of China(61033011 ; National Key Technology Research and Development Program(2012BAI34B02) ; 61210009)
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS记录号	WOS:000363233000020
引用统计	被引频次：25[WOS] [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	http://ir.ia.ac.cn/handle/173211/10491
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
作者单位	1.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
第一作者单位	中国科学院自动化研究所
推荐引用方式 GB/T 7714	Qiao, Hong,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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