In this paper, we present novel techniques that improve the computational and memory efficiency of algorithms for solving multi-label energy functions arising from discrete MRFs or CRFs. These methods are motivated by the observations that the performance of minimization algorithms depends on: (a) the initialization used for the primal and dual variables; and (b) the number of primal variables involved in the energy function. Our first method (dynamic \u03b1- expansion) works by \u2018recycling\u2019 results from previous problem instances. The second method simplifies the energy function by \u2018reducing\u2019 the number of unknown variables, and can also be used to generate a good initialization for the dynamic \u03b1-expansion algorithm by \u2018reusing\u2019 dual variables. We test the performance of our methods on energy functions encountered in the problems of stereo matching, and colour and<\/p>\n
We test the performance of our methods on energy functions encountered in the problems of stereo matching, and colour and object based segmentation. Experimental results show that our methods achieve a substantial improvement in the performance of \u03b1-expansion, as well as other popular algorithms such as sequential tree-reweighted message passing, and max-product belief propagation. In most cases we achieve a 10-15 times speed-up in the computation time. Our modified \u03b1-expansion algorithm provides similar performance to Fast-PD [15]. However, it is much simpler and can be made orders of magnitude faster by using the initialization schemes proposed in the paper.<\/p>\n","protected":false},"excerpt":{"rendered":"
In this paper, we present novel techniques that improve the computational and memory efficiency of algorithms for solving multi-label energy functions arising from discrete MRFs or CRFs. These methods are motivated by the observations that the performance of minimization algorithms depends on: (a) the initialization used for the primal and dual variables; and (b) the […]<\/p>\n","protected":false},"featured_media":0,"template":"","meta":{"msr-url-field":"","msr-podcast-episode":"","msrModifiedDate":"","msrModifiedDateEnabled":false,"ep_exclude_from_search":false,"_classifai_error":"","msr-author-ordering":null,"msr_publishername":"IEEE","msr_publisher_other":"","msr_booktitle":"","msr_chapter":"","msr_edition":"Computer Vision and Pattern Recognition, 2008. CVPR 2008. 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