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.
An erratum to this chapter can be found at http://dx.doi.org/10.1007/11427445_150 .
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Li, L.S., Huang, D., Zheng, C., Sun, Z. (2005). Image Feature Extraction Based on an Extended Non-negative Sparse Coding Neural Network Model. In: Wang, J., Liao, XF., Yi, Z. (eds) Advances in Neural Networks – ISNN 2005. ISNN 2005. Lecture Notes in Computer Science, vol 3497. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11427445_130
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DOI: https://doi.org/10.1007/11427445_130
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-25913-8
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