Image feature extraction based on an extended non-negative sparse coding neural network model

Authors:
Li Shang;Deshuang Huang;Chunhou Zheng;Zhanli Sun
Affiliations:
Hefei Institute of Intelligent Machines, Chinese Academy of Sciences and Department of Automation, University of Science and Technology of China, Hefei, China;Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, China;Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, China;Hefei Institute of Intelligent Machines, Chinese Academy of Sciences, Hefei, China
Venue:
ISNN'05 Proceedings of the Second international conference on Advances in neural networks - Volume Part II
Year:
2005

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Image Feature Extraction by Sparse Coding and Independent Component Analysis

ICPR '98 Proceedings of the 14th International Conference on Pattern Recognition-Volume 2 - Volume 2

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Abstract

This paper proposes an extended non-negative sparse coding (NNSC) neural network model for natural image feature extraction. The advantage for our model is to be able to ensure to converge to the basis vectors, which can respond well to the edge of the original images. Using the criteria of objective fidelity and the negative entropy, the validity of image feature extraction is testified. Furthermore, compared with independent component analysis (ICA) technique, the experimental results show that the quality of reconstructed images obtained by our method outperforms the ICA method.