Image feature extraction by sparse coding and independent component analysis

نویسندگان

  • Aapo Hyvärinen
  • Erkki Oja
  • Patrik O. Hoyer
  • Jarmo Hurri
چکیده

Sparse coding is a method for nding a representation of data in which each of the components of the representation is only rarely signiicantly active. Such a representation is closely related to the techniques of independent component analysis and blind source separation. In this paper, we investigate the application of sparse coding for image feature extraction. We show how sparse coding can be used to extract wavelet-like features from natural image data. As an application of such a feature extraction scheme, we show how to apply a soft-thresholding operator on the components of sparse coding in order to reduce Gaussian noise. Methods based on sparse coding have the important beneet over wavelet methods that the features are determined solely by the statistical properties of the data, while the wavelet transformation relies heavily on certain abstract mathematical properties that may be only weakly related to the properties of the natural data.

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تاریخ انتشار 1998