31 July 2002 Applications of independent component analysis to image feature extraction
Author Affiliations +
Proceedings Volume 4875, Second International Conference on Image and Graphics; (2002) https://doi.org/10.1117/12.477183
Event: Second International Conference on Image and Graphics, 2002, Hefei, China
Abstract
Independent Component Analysis (ICA) is a new signal processing method developed recently which analyzes the data from a statistical point of view. In ICA, one can try to express a set of random variables as linear combinations of statistically independent components. In this paper, ICA is applied to image feature extraction, and the information maximization algorithm is performed to optimize the results. From the results, it can be seen that the extracted features represent the image data in a natural way. In addition, the ICA basis vectors are localized and oriented, and sensitive to lines and edges of varying thickness of images. As an application of these extracted features, another denoising experiment is done. In this experiment a Gaussian noise is reduced by applying a soft-thresholding operator on the extracted ICA coefficients.
© (2002) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE). Downloading of the abstract is permitted for personal use only.
Ling Fan, Ling Fan, Fei Long, Fei Long, Dao-xin Zhang, Dao-xin Zhang, Xiao-jing Guo, Xiao-jing Guo, Xiao-pei Wu, Xiao-pei Wu, } "Applications of independent component analysis to image feature extraction", Proc. SPIE 4875, Second International Conference on Image and Graphics, (31 July 2002); doi: 10.1117/12.477183; https://doi.org/10.1117/12.477183
PROCEEDINGS
6 PAGES


SHARE
RELATED CONTENT

Efficient image representations and features
Proceedings of SPIE (March 14 2013)
Issues in nonuniform filter banks
Proceedings of SPIE (December 04 2000)
ICA-based robust logo image watermarking
Proceedings of SPIE (June 22 2004)
Learning optimal features for visual pattern recognition
Proceedings of SPIE (February 15 2007)
A wavelet filter performance criterion
Proceedings of SPIE (November 01 2004)

Back to Top