Unsupervised Texture Classification And Segmentation

V.P.Subramanyam Rallabandi, S.K.Sett
2007 Zenodo  
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 textures, the algorithm can learn
more » ... sis functions for images that capture the statistically significant structure intrinsic in the images. We apply this technique to the problem of unsupervised texture classification and segmentation.
doi:10.5281/zenodo.1332180 fatcat:gx6hru6j7bbejeferxj4aqpame