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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Batra, D. Kohli, P. |
| Copyright Year | 2011 |
| Description | Author affiliation: Microsoft Research Cambridge (Kohli, P.) || Toyota Technological Institute at Chicago (Batra, D.) |
| Abstract | This paper presents a new adaptive graph-cut based move-making algorithm for energy minimization. Traditional move-making algorithms such as Expansion and Swap operate by searching for better solutions in some predefined moves spaces around the current solution. In contrast, our algorithm uses the primal-dual interpretation of the Expansion-move algorithm to adaptively compute the best move-space to search over. At each step, it tries to greedily find the move-space that will lead to biggest decrease in the primal-dual gap. We test different variants of our algorithm on a variety of image labelling problems such as object segmentation and stereo. Experimental results show that our adaptive strategy significantly outperforms the conventional Expansion-move algorithm, in some cases cutting the runtime by 50%. |
| Starting Page | 1865 |
| Ending Page | 1872 |
| File Size | 1591503 |
| Page Count | 8 |
| File Format | |
| ISBN | 9781457703942 |
| ISSN | 10636919 |
| e-ISBN | 9781457703959 |
| DOI | 10.1109/CVPR.2011.5995449 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2011-06-20 |
| Publisher Place | USA |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Labeling Heuristic algorithms Inference algorithms Object segmentation Proposals Stereo vision Visualization |
| Content Type | Text |
| Resource Type | Article |
| Subject | Computer Vision and Pattern Recognition Software |
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