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	Object class recognition based on compressive sensing with sparse features inspired by hierarchical model in visual cortex
	Lu, Pei1,2,3; Xu, Zhiyong1; Yu, Huapeng1,3; Chang, Yongxin1,3; Fu, Chengyu1; Shao, Jianxin4
	2012
会议名称	Proceedings of SPIE: Optoelectronic Imaging and Multimedia Technology II
会议日期	2012
卷号	8558
页码	85581X
通讯作者	Lu, P. (lupei0@126.com)
中文摘要	According to models of object recognition in cortex, the brain uses a hierarchical approach in which simple, low-level features having high position and scale specificity are pooled and combined into more complex, higher-level features having greater location invariance. At higher levels, spatial structure becomes implicitly encoded into the features themselves, which may overlap, while explicit spatial information is coded more coarsely. In this paper, the importance of sparsity and localized patch features in a hierarchical model inspired by visual cortex is investigated. As in the model of Serre, Wolf, and Poggio, we first apply Gabor filters at all positions and scales; feature complexity and position/scale invariance are then built up by alternating template matching and max pooling operations. In order to improve generalization performance, the sparsity is proposed and data dimension is reduced by means of compressive sensing theory and sparse representation algorithm. Similarly, within computational neuroscience, adding the sparsity on the number of feature inputs and feature selection is critical for learning biologically model from the statistics of natural images. Then, a redundancy dictionary of patch-based features that could distinguish object class from other categories is designed and then object recognition is implemented by the process of iterative optimization. The method is test on the UIUC car database. The success of this approach suggests a proof for the object class recognition in visual cortex. © Copyright SPIE.
英文摘要	According to models of object recognition in cortex, the brain uses a hierarchical approach in which simple, low-level features having high position and scale specificity are pooled and combined into more complex, higher-level features having greater location invariance. At higher levels, spatial structure becomes implicitly encoded into the features themselves, which may overlap, while explicit spatial information is coded more coarsely. In this paper, the importance of sparsity and localized patch features in a hierarchical model inspired by visual cortex is investigated. As in the model of Serre, Wolf, and Poggio, we first apply Gabor filters at all positions and scales; feature complexity and position/scale invariance are then built up by alternating template matching and max pooling operations. In order to improve generalization performance, the sparsity is proposed and data dimension is reduced by means of compressive sensing theory and sparse representation algorithm. Similarly, within computational neuroscience, adding the sparsity on the number of feature inputs and feature selection is critical for learning biologically model from the statistics of natural images. Then, a redundancy dictionary of patch-based features that could distinguish object class from other categories is designed and then object recognition is implemented by the process of iterative optimization. The method is test on the UIUC car database. The success of this approach suggests a proof for the object class recognition in visual cortex. © Copyright SPIE.
收录类别	EI
语种	英语
ISSN号	0277786X
内容类型	会议论文
源URL	[http://ir.ioe.ac.cn/handle/181551/7696]
专题	光电技术研究所_光电探测与信号处理研究室（五室）
作者单位	1.Institute of Optics and Electronics, Chinese Academy of Sciences, Chengdu 610209, China 2.College of Information Science and Technology, Shihezi University, Shihezi 832000, China 3.Graduate University, Chinese Academy of Sciences, Beijing 100049, China 4.Normal College, Shihezi University, Shihezi 832000, China
推荐引用方式 GB/T 7714	Lu, Pei,Xu, Zhiyong,Yu, Huapeng,et al. Object class recognition based on compressive sensing with sparse features inspired by hierarchical model in visual cortex[C]. 见:Proceedings of SPIE: Optoelectronic Imaging and Multimedia Technology II. 2012.

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