Sciweavers

Share
TIP
2002

Unsupervised image classification, segmentation, and enhancement using ICA mixture models

9 years 10 months ago
Unsupervised image classification, segmentation, and enhancement using ICA mixture models
An unsupervised classification algorithm is derived by modeling observed data as a mixture of several mutually exclusive classes that are each described by linear combinations of independent, non-Gaussian densities. The algorithm estimates the data density in each class by using parametric nonlinear functions that fit to the non-Gaussian structure of the data. This improves classification accuracy compared with standard Gaussian mixture models. When applied to images, the algorithm can learn efficient codes (basis functions) for images that capture the statistically significant structure intrinsic in the images. We apply this technique to the problem of unsupervised classification, segmentation, and denoising of images. We demonstrate that this method was effective in classifying complex image textures such as natural scenes and text. It was also useful for denoising and filling in missing pixels in images with complex structures. The advantage of this model is that image codes can be ...
Te-Won Lee, Michael S. Lewicki
Added 23 Dec 2010
Updated 23 Dec 2010
Type Journal
Year 2002
Where TIP
Authors Te-Won Lee, Michael S. Lewicki
Comments (0)
books