• 中国计算机学会会刊
  • 中国科技核心期刊
  • 中文核心期刊
论文

Image Base Learning Based on Improved NonNegative Sparse Coding

  • CHAO Yong-Guo ,
  • DAI Fang ,
  • HAN Shu-Ran ,
  • HE Jing
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  • (Xi’an University of Technology,Xi’an 710054,China)

Received date: 2008-09-03

  Revised date: 2008-11-18

  Online published: 2010-01-18

Abstract

Image base learning is one of the important ways of image feature extraction and image expression. Nonnegative sparse coding not only features  the adaptability of standard sparse coding, spatial localization, orientation, and bandpass in different spatial frequency bands, but also responds to mammal's visual mechanism well. On the basis of the nonnegative sparse coding, this paper  joins the image structure information using the experience modality decomposition technology, proposes a combination of EMD and the nonnegative 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 nonnegative sparse coding, but express the image's structure information well.

Cite this article

CHAO Yong-Guo , DAI Fang , HAN Shu-Ran , HE Jing . Image Base Learning Based on Improved NonNegative Sparse Coding[J]. Computer Engineering & Science, 2010 , 32(1) : 77 -79 . DOI: 10.3969/j.issn.1007130X.2010.

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