Introducing Memory and Association Mechanism Into a Biologically Inspired Visual Model

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

	Introducing Memory and Association Mechanism Into a Biologically Inspired Visual Model
	Qiao, Hong; Li, Yinlin; Tang, Tang; Wang, Peng
发表期刊	IEEE TRANSACTIONS ON CYBERNETICS
	2014-09-01
卷号	44 期号:9 页码:1485-1496
文章类型	Article
摘要	A famous biologically inspired hierarchical model (HMAX model), which was proposed recently and corresponds to V1 to V4 of the ventral pathway in primate visual cortex, has been successfully applied to multiple visual recognition tasks. The model is able to achieve a set of position- and scale-tolerant recognition, which is a central problem in pattern recognition. In this paper, based on some other biological experimental evidence, we introduce the memory and association mechanism into the HMAX model. The main contributions of the work are: 1) mimicking the active memory and association mechanism and adding the top down adjustment to the HMAX model, which is the first try to add the active adjustment to this famous model and 2) from the perspective of information, algorithms based on the new model can reduce the computation storage and have a good recognition performance. The new model is also applied to object recognition processes. The primary experimental results show that our method is efficient with a much lower memory requirement.
关键词	Association Biologically Inspired Visual Model Memory Object Recognition
WOS标题词	Science & Technology ; Technology
关键词[WOS]	MONKEY INFEROTEMPORAL CORTEX ; OBJECT RECOGNITION ; DECLARATIVE MEMORY ; ATTENTION MODEL ; FACIAL FEATURES ; SEMANTIC MEMORY ; TEMPORAL-LOBE ; FAMILIARITY ; MOTION ; DISCRIMINATION
收录类别	SCI
语种	英语
WOS研究方向	Computer Science
WOS类目	Computer Science, Artificial Intelligence ; Computer Science, Cybernetics
WOS记录号	WOS:000342227500001
引用统计	被引频次：46[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/173211/3028
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	Chinese Acad Sci, Inst Automat, 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,Li, Yinlin,Tang, Tang,et al. Introducing Memory and Association Mechanism Into a Biologically Inspired Visual Model[J]. IEEE TRANSACTIONS ON CYBERNETICS,2014,44(9):1485-1496.
APA	Qiao, Hong,Li, Yinlin,Tang, Tang,&Wang, Peng.(2014).Introducing Memory and Association Mechanism Into a Biologically Inspired Visual Model.IEEE TRANSACTIONS ON CYBERNETICS,44(9),1485-1496.
MLA	Qiao, Hong,et al."Introducing Memory and Association Mechanism Into a Biologically Inspired Visual Model".IEEE TRANSACTIONS ON CYBERNETICS 44.9(2014):1485-1496.

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