<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>47</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Carsten Rother</style></author><author><style face="normal" font="default" size="100%">Kolmogorov, Vladimir</style></author><author><style face="normal" font="default" size="100%">Minka, Tom</style></author><author><style face="normal" font="default" size="100%">Blake, Andrew</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Cosegmentation of image pairs by histogram matching - Incorporating a global constraint into MRFs</style></title><secondary-title><style face="normal" font="default" size="100%">Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2006</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://research.microsoft.com/vision/cambridge/</style></url></web-urls></urls><volume><style face="normal" font="default" size="100%">1</style></volume><pages><style face="normal" font="default" size="100%">994–1000</style></pages><isbn><style face="normal" font="default" size="100%">0769525970</style></isbn><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We introduce the term cosegmentation which denotes the task of segmenting simultaneously the common parts of an image pair. A generative model for cosegmentation is presented. Inference in the model leads to minimizing an energy with an MRF term encoding spatial coherency and a global constraint which attempts to match the appearance histograms of the common parts. This energy has not been proposed previously and its optimization is challenging and NP-hard. For this problem a novel optimization scheme which we call trust region graph cuts is presented. We demonstrate that this framework has the potential to improve a wide range of research: Object driven image retrieval, video tracking and segmentation, and interactive image editing. The power of the framework lies in its generality, the common part can be a rigid/non-rigid object (or scene), observed from different viewpoints or even similar objects of the same class. © 2006 IEEE.</style></abstract></record></records></xml>