Solving MultiLabel MRFs using Incremental alpha-expansion on GPU

1. Solving MultiLabel MRFs using Incremental alpha-expansion on GPU Vibhav Vineet and P. J. Narayanan Dynamic Graph-Cuts Multi-labeling Problems Energy Minimization Method Reparameter-rization Updation Better Initialized Residual Graph of Next MRF Instance Residual Graph of Previous MRF Updated Residual Graph Flow Residual Flow Incremental alpha Expansion on GPU Basic Algorithm On GPU 1 l 1 2 l 2 1 2 l • Initialize the Graph • For First Cycle • For Lable 1: • Construct Graph and perform Graph Cut on GPU • Save final excess flow • For Labels l from2 to L: • Construct graph and update and repratemeterize flow based on and perform Graph Cuts on GPU • Save final excess flow • For later cycle i and iteration k: • Construct graph and update and reparameterize based on and perform Graph Cuts on GPU C C C G G G G G G G G G 1 k 2 2 1 k 2 k k 2 1 1 • 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. 2/-1 2/0 2/3 3/1 3/1 3/1 • Two Steps of Updation and • Re-parameterization • Easily parallelizable 4/0 4/0 4/0 Basic Graph Cuts On GPU 5/3 5/3 5/3 Dynamic Graph Cut Performed 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 1 k l k l-1 3/0 3/0 3/1 G G G G G 7/0 7/0 7/0 i i-1 1 1 1 Iteration within a Cycle Cycle 0 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 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