<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>5</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Zoltan Kato</style></author><author><style face="normal" font="default" size="100%">Ting Chuen Pong</style></author><author><style face="normal" font="default" size="100%">Song Guo Qiang</style></author></authors><secondary-authors><author><style face="normal" font="default" size="100%">IEEE</style></author></secondary-authors></contributors><titles><title><style face="normal" font="default" size="100%">Unsupervised segmentation of color textured images using a multi-layer MRF model</style></title><secondary-title><style face="normal" font="default" size="100%">ICIP 2003: IEEE International Conference on Image Processing</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2003</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2003///</style></date></pub-dates></dates><publisher><style face="normal" font="default" size="100%">IEEE</style></publisher><pages><style face="normal" font="default" size="100%">961 - 964</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;Herein, we propose a novel multi-layer Markov random field (MRF) image segmentation model which aims at combining color and texture features: Each feature is associated to a so called feature layer, where an MRF model is defined using only the corresponding feature. A special layer is assigned to the combined MRF model. This layer interacts with each feature layer and provides the segmentation based on the combination of different features. The model is quite generic and isn't restricted to a particular texture feature. Herein we will test the algorithm using Gabor and MRSAR texture features. Furthermore, the algorithm automatically estimates the number of classes at each layer (there can be different classes at different layers) and the associated model parameters.&lt;/p&gt;</style></abstract><notes><style face="normal" font="default" size="100%">ScopusID: 0344666539doi: 10.1109/ICIP.2003.1247124</style></notes></record></records></xml>