DocumentCode :
2771597
Title :
Image feature extraction by sparse coding and independent component analysis
Author :
Hyvarinen, Aapo ; Oja, Erkki ; Hoyer, Patrik ; Hurri, Jarmo
Author_Institution :
Lab. of Comput. & Inf. Sci., Helsinki Univ. of Technol., Espoo, Finland
Volume :
2
fYear :
1998
fDate :
16-20 Aug 1998
Firstpage :
1268
Abstract :
Sparse coding is a method for finding a representation of data in which each of the components of the representation is only rarely significantly 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 benefit 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
Keywords :
Gaussian noise; feature extraction; image coding; image recognition; Gaussian noise; feature extraction; image recognition; independent component analysis; sparse coding; Blind source separation; Data mining; Data models; Feature extraction; Frequency; Image coding; Independent component analysis; Information analysis; Probability distribution; Vectors;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on
Conference_Location :
Brisbane, Qld.
ISSN :
1051-4651
Print_ISBN :
0-8186-8512-3
Type :
conf
DOI :
10.1109/ICPR.1998.711932
Filename :
711932
Link To Document :
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