Distributed non-convex ADMM-inference in large-scale random fields

Abstract

We propose a parallel and distributed algorithm for solving discrete labeling problems in large scale random fields. Our approach is motivated by the following observations: i) very large scale image and video processing problems, such as labeling dozens of million pixels with thousands of labels, are routinely faced in many application domains; ii) the computational complexity of the current state-of-the-art inference algorithms makes them impractical to solve such large scale problems; iii) modern parallel and distributed systems provide high computation power at low cost. At the core of our algorithm is a tree-based decomposition of the original optimization problem which is solved using a non convex form of the method of alternating direction method of multipliers (ADMM). This allows efficient parallel solving of resulting sub-problems. We evaluate the efficiency and accuracy offered by our algorithm on several benchmark lowlevel vision problems, on both CPU and Nvidia GPU. We consistently achieve a factor of speed-up compared to dual decomposition (DD) approach and other ADMM-based approaches.