Computer Engineering and Applications ›› 2011, Vol. 47 ›› Issue (4): 160-164.DOI: 10.3778/j.issn.1002-8331.2011.04.044

• 图形、图像、模式识别 • Previous Articles     Next Articles

Modified non-negative sparse coding neural network model and its applications


  1. Department of Electronic Information Engineering,Suzhou Vocational University,Suzhou,Jiangsu 215104,China
  • Received:2009-08-18 Revised:2009-10-05 Online:2011-02-01 Published:2011-02-01
  • Contact: SHANG Li


尚 丽   

  1. 苏州市职业大学 电子信息工程系,江苏 苏州 215104
  • 通讯作者: 尚 丽

Abstract: This paper proposes a modified Non-Negative Sparse Coding(NNSC) neural network model based on the Normal Inverse Gaussian(NIG) density and robust Principal Component Analysis(PCA).This model implements in fact a two-phase learning.Using this model,the V1 receptive fields of vision-perceptional system can be modeled successfully.Compared with Hoyer’s NNSC model,the NIG-based NNSC network behaves stronger capacity of adapting to the statistical properties of natural data.Otherwise,exploiting the similar wavelet shrinkage principle,this model can denoise efficiently the additive Gaussian noise in an image.The simulations of coding on natural images demonstrate this model’s plausibility in neuroscience view and feasibility in practical computation.

Key words: Normal Inverse Gaussian(NIG) density model, robust Principal Component Analysis(PCA), non-negative sparse coding, non-negative matrix factorization, feature extraction, image denoising

摘要: 提出了一种改进的基于NIG(Normal Inverse Gaussian)密度和稳健主成分分析(PCA)的非负稀疏编码(NNSC)神经网络模型,该模型实质上实现了一个二阶段的学习过程。并利用这个模型成功地建模了视觉感知系统V1区的感受野。该NNSC模型具有很强的自适应于自然数据统计特性的能力。另外,利用类似小波收缩法去噪原理,该模型能够有效地去除图像中的高斯加性噪声,对自然图像编码的仿真实验也表明了该模型在生物学上的合理性和可行性。

关键词: 正态逆高斯(NIG)密度模型, 稳健主成分分析, 非负稀疏编码, 非负矩阵分解, 特征提取, 图像去噪

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