{"publication_status":"published","_id":"3188","extern":1,"day":"05","date_updated":"2021-01-12T07:41:40Z","date_published":"2006-07-05T00:00:00Z","conference":{"name":"CVPR: Computer Vision and Pattern Recognition"},"status":"public","date_created":"2018-12-11T12:01:54Z","citation":{"ieee":"C. Rother, V. Kolmogorov, T. Minka, and A. Blake, “Cosegmentation of image pairs by histogram matching - Incorporating a global constraint into MRFs,” presented at the CVPR: Computer Vision and Pattern Recognition, 2006, pp. 993–1000.","ama":"Rother C, Kolmogorov V, Minka T, Blake A. Cosegmentation of image pairs by histogram matching - Incorporating a global constraint into MRFs. In: IEEE; 2006:993-1000. doi:<a href=\"https://doi.org/10.1109/CVPR.2006.91\">10.1109/CVPR.2006.91</a>","apa":"Rother, C., Kolmogorov, V., Minka, T., &#38; Blake, A. (2006). Cosegmentation of image pairs by histogram matching - Incorporating a global constraint into MRFs (pp. 993–1000). Presented at the CVPR: Computer Vision and Pattern Recognition, IEEE. <a href=\"https://doi.org/10.1109/CVPR.2006.91\">https://doi.org/10.1109/CVPR.2006.91</a>","chicago":"Rother, Carsten, Vladimir Kolmogorov, Thomas Minka, and Andrew Blake. “Cosegmentation of Image Pairs by Histogram Matching - Incorporating a Global Constraint into MRFs,” 993–1000. IEEE, 2006. <a href=\"https://doi.org/10.1109/CVPR.2006.91\">https://doi.org/10.1109/CVPR.2006.91</a>.","short":"C. Rother, V. Kolmogorov, T. Minka, A. Blake, in:, IEEE, 2006, pp. 993–1000.","ista":"Rother C, Kolmogorov V, Minka T, Blake A. 2006. Cosegmentation of image pairs by histogram matching - Incorporating a global constraint into MRFs. CVPR: Computer Vision and Pattern Recognition, 993–1000.","mla":"Rother, Carsten, et al. <i>Cosegmentation of Image Pairs by Histogram Matching - Incorporating a Global Constraint into MRFs</i>. IEEE, 2006, pp. 993–1000, doi:<a href=\"https://doi.org/10.1109/CVPR.2006.91\">10.1109/CVPR.2006.91</a>."},"quality_controlled":0,"title":"Cosegmentation of image pairs by histogram matching - Incorporating a global constraint into MRFs","publist_id":"3493","publisher":"IEEE","month":"07","type":"conference","page":"993 - 1000","year":"2006","author":[{"full_name":"Rother, Carsten","first_name":"Carsten","last_name":"Rother"},{"full_name":"Vladimir Kolmogorov","first_name":"Vladimir","id":"3D50B0BA-F248-11E8-B48F-1D18A9856A87","last_name":"Kolmogorov"},{"full_name":"Minka, Thomas P","first_name":"Thomas","last_name":"Minka"},{"full_name":"Blake, Andrew","first_name":"Andrew","last_name":"Blake"}],"doi":"10.1109/CVPR.2006.91","abstract":[{"lang":"eng","text":"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."}]}