Image representation in visual cortex and high nonlinear approximation

Authors:
Shan Tan;Xiangrong Zhang;Shuang Wang;Licheng Jiao
Affiliations:
National Key Lab for Radar Signal Processing and Institute of Intelligent, Information Processing, Xidian University, Xi'an, China;National Key Lab for Radar Signal Processing and Institute of Intelligent, Information Processing, Xidian University, Xi'an, China;National Key Lab for Radar Signal Processing and Institute of Intelligent, Information Processing, Xidian University, Xi'an, China;National Key Lab for Radar Signal Processing and Institute of Intelligent, Information Processing, Xidian University, Xi'an, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in Neural Networks - Volume Part I
Year:
2005

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Abstract

We briefly review the “sparse coding” principle employed in the sensory information processing system of mammals and focus on the phenomenon that such principle is realized through over-complete representation strategy in primary sensory cortical areas (V1). Considering the lack of quantitative analysis of how many gains in sparsenality the over-complete representation strategy brings in neuroscience, in this paper, we give a quantitative analysis from the viewpoint of nonlinear approximation. The result shows that the over-complete strategy can provide sparser representation than the complete strategy.