Computer Engineering & Science >
Image Base Learning Based on Improved NonNegative Sparse Coding
Received date: 2008-09-03
Revised date: 2008-11-18
Online published: 2010-01-18
Image base learning is one of the important ways of image feature extraction and image expression. Nonnegative sparse coding not only features the adaptability of standard sparse coding, spatial localization, orientation, and bandpass in different spatial frequency bands, but also responds to mammal's visual mechanism well. On the basis of the nonnegative sparse coding, this paper joins the image structure information using the experience modality decomposition technology, proposes a combination of EMD and the nonnegative sparse coding algorithm, and ensures the sparseness of the coefficient matrix and the structural characteristics of the image bases extracted. The learned image bases not only have the characteristic of nonnegative sparse coding, but express the image's structure information well.
CHAO Yong-Guo , DAI Fang , HAN Shu-Ran , HE Jing . Image Base Learning Based on Improved NonNegative Sparse Coding[J]. Computer Engineering & Science, 2010 , 32(1) : 77 -79 . DOI: 10.3969/j.issn.1007130X.2010.
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