NDLI: A novel sparse coding model based on structural similarity

Content Provider	IEEE Xplore Digital Library
Author	Zhiqing Li Zhiping Shi Xi Liu Zhongzhi Shi
Copyright Year	2010
Description	Author affiliation: Key Laboratory of Intelligent Information Processing, Institute of Computing Technology, Chinese Academy of Sciences, Beijing 100190, China (Zhiqing Li; Zhiping Shi; Xi Liu; Zhongzhi Shi)
Abstract	Understanding and modeling the function of the neurons and neural systems are primary goal of systems neuroscience. Sparse coding theory demonstrates that the neurons in primary visual cortex form a sparse representation of natural scenes in the viewpoint of statistics. In this paper, we propose a novel sparse coding model based on structural similarity (SS_SC) for natural image feature extraction. The advantage for our model is to be able to preserve structural information from a scene, which human visual perception is highly adapted for. Using the proposed sparse coding model, the validity of image feature extraction is testified. Furthermore, compared with standard sparse coding (SC) model, the experimental results show that the quality of reconstructed images obtained by our method outperforms the SC method.
Starting Page	4170
Ending Page	4173
File Size	537200
Page Count	4
File Format	PDF
ISBN	9781424442959
ISSN	15206149
DOI	10.1109/ICASSP.2010.5495707
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-03-14
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Brain modeling Image coding Neurons Layout Feature extraction Neuroscience Codes Statistics Humans Visual perception biological visual system Natural image sparse coding structural similarity computational model
Content Type	Text
Resource Type	Article

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