Color Channels Decorrelation by ICA Transformation in the Wavelet Domain for Color Texture Analysis and Synthesis
Abstract
In this paper, we developed a color model to cancel the dependency between color channels, which enables us to separate spectral processing from, spatial processing. We introduced Independent Component Analysis (ICA) transformation in the wavelet domain to decorrelate the subband color joint statistics. The decorrelated joint color conditional histograms display scaling of variance. Gaussian Scale Mixture (GSM) was used to model the subband color statistics and a normalization scheme was adapted to cancel the pair-wise color subband statistical dependency. This color model was combined with the Portilla/Simoncelli texture model to construct the color texture model. Based on this model, features were extracted and the corresponding color texture synthesis scheme was developed.
Cite
Text
Liang et al. "Color Channels Decorrelation by ICA Transformation in the Wavelet Domain for Color Texture Analysis and Synthesis." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2000. doi:10.1109/CVPR.2000.855875Markdown
[Liang et al. "Color Channels Decorrelation by ICA Transformation in the Wavelet Domain for Color Texture Analysis and Synthesis." IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2000.](https://mlanthology.org/cvpr/2000/liang2000cvpr-color/) doi:10.1109/CVPR.2000.855875BibTeX
@inproceedings{liang2000cvpr-color,
title = {{Color Channels Decorrelation by ICA Transformation in the Wavelet Domain for Color Texture Analysis and Synthesis}},
author = {Liang, Yufeng and Simoncelli, Eero P. and Lei, Zhibin},
booktitle = {IEEE/CVF Conference on Computer Vision and Pattern Recognition},
year = {2000},
pages = {1606-1610},
doi = {10.1109/CVPR.2000.855875},
url = {https://mlanthology.org/cvpr/2000/liang2000cvpr-color/}
}