Cosegmentation of Image Pairs by Histogram Matching - Incorporating a Global Constraint into MRFs

Carsten Rother, Vladimir Kolmogorov, Tom Minka and Andrew Blake.

In IEEE Conference on Computer Vision and Pattern Recognition (CVPR), June 2006.


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.


More detailed version: Microsoft technical report MSR-TR-2006-36