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Proceedings Paper

Object class recognition based on compressive sensing with sparse features inspired by hierarchical model in visual cortex
Author(s): Pei Lu; Zhiyong Xu; Huapeng Yu; Yongxin Chang; Chengyu Fu; Jianxin Shao
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Paper Abstract

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.

Paper Details

Date Published: 30 November 2012
PDF: 9 pages
Proc. SPIE 8558, Optoelectronic Imaging and Multimedia Technology II, 85581X (30 November 2012); doi: 10.1117/12.999501
Show Author Affiliations
Pei Lu, The Institute of Optics and Electronics (China)
Shihezi Univ. (China)
Graduate Univ. of Chinese Academy of Sciences (China)
Zhiyong Xu, The Institute of Optics and Electronics (China)
Huapeng Yu, The Institute of Optics and Electronics (China)
Graduate Univ. of Chinese Academy of Sciences (China)
Yongxin Chang, The Institute of Optics and Electronics (China)
Graduate Univ. of Chinese Academy of Sciences (China)
Chengyu Fu, Institute of Optics and Electronics (China)
Jianxin Shao, Shihezi Univ. (China)


Published in SPIE Proceedings Vol. 8558:
Optoelectronic Imaging and Multimedia Technology II
Tsutomu Shimura; Guangyu Xu; Linmi Tao; Jesse Zheng, Editor(s)

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