Multi-labeling Problems Solving MultiLabel MRFs using Incremental alpha-expansion on GPU Vibhav Vineet and P. J. Narayanan Dynamic Graph-Cuts Multi-labeling Problems 3/1 2/0 7/0 4/0 5/3 3/1 2/3 3/0 7/0 4/0 5/3 3/1 2/-1 3/0 7/0 4/0 5/3 Energy Minimization Method Reparameter-rization Updation Better Initialized Residual Graph of Next MRF Instance Residual Graph of Previous MRF Updated Residual Graph Low level vision problems involve assigning a label from a set to each pixel in the image Mapped as an energy minimization problem defined over a discrete MRF. Two Steps of Updation and Re-parameterization Easily parallelizable Flow Residual Flow Incremental alpha Expansion on GPU Basic Algorithm On GPU Initialize the Graph For First Cycle For Lable 1: Construct Graph and perform Graph Cut on GPU Save final excess flow For Labels l from 2 to L: Construct graph and update and repratemeterize flow based on and perform Graph Cuts on GPU For later cycle i and iteration k: Construct graph and update and reparameterize based on and perform Graph Cuts on GPU G 2 1 G 1 C 1 G l 1 Dynamic Graph Cut Performed on GPU G 1 C 2 G 2 G l 2 G 1 2 Basic Graph Cuts On GPU Graph Cuts on GPU Push-Relabel Algorithm Push Operation: Each Vertex pushing flow to its neighbor; easily parallelizable Relabel Operation: Adjustment of heights Performing local relabel G l 1 G l-1 1 G 1 k G 2 k G l k C k G k i Iteration within a Cycle G k i-1 Cycle Stochastic Cuts Incremental alpha Expansion A complete parallel way of solving multi-label MRF Energy Function Computed on GPU Graph Construction on GPU Dynamic Graph Cuts: Updation and Re-parameterization done on GPU Graph Cuts performed on GPU Memory Requirement Need to store original graph and residual graphs for all the labels for the previous cycle MRF constitutes both simple and difficult variables Simple Variables settle quickly Based on this observation, block wise processing of the pixels Delaying the processing of the block which is unlikely to exchange any flow with neighbors Result Section Image Size #ofLabels Boykov Dynamic Incremental Tsukuba 384 x 288 16 5400 3230 950 Teddy 450 x 375 60 44200 21400 6890 Penguin 122 x 179 256 34390 12310 5120 Panorama 1071 x480 7 30593 15988 6520 Teddy; stereo Penguin; De-noising Panorama; Photomontage Tsukuba; stereo Experiments conducted on stereo, denoising, photomontage etc. Datasets from Middlebury MRF page. Observed an speed-up of 4-6 times on different datasets Code available at: http://cvit.iiit.ac.in/index.php?page=resources Hardware used: GTX 280 GPU Comparison Done with Boykov’s method and Dynamic alpha-expansion on CPU Center for Visual Information Technology International Institute of Information Technology Hyderabad http://cvit.iiit.ac.in