@article{(Open Science Index):https://publications.waset.org/pdf/4391,
	  title     = {Unsupervised Texture Classification and Segmentation},
	  author    = {V.P.Subramanyam Rallabandi and  S.K.Sett},
	  country	= {},
	  institution	= {},
	  abstract     = {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 basis
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.},
	    journal   = {International Journal of Computer and Information Engineering},
	  volume    = {1},
	  number    = {11},
	  year      = {2007},
	  pages     = {3551 - 3554},
	  ee        = {https://publications.waset.org/pdf/4391},
	  url   	= {https://publications.waset.org/vol/11},
	  bibsource = {https://publications.waset.org/},
	  issn  	= {eISSN: 1307-6892},
	  publisher = {World Academy of Science, Engineering and Technology},
	  index 	= {Open Science Index 11, 2007},
	}