Unsupervised Markovian Segmentation on Graphics Hardware

Abstract. This contribution shows how unsupervised Markovian segmentation techniques can be accelerated when implemented on graphics hardware equipped with a Graphics Processing Unit (GPU). Our strategy exploits the intrinsic properties of local interactions between sites of a Markov Random Field model with the parallel computation ability of a GPU. This paper explains how classical iterative site-wise-update algorithms commonly used to optimize global Markovian cost functions can be efficiently implemented in parallel by fragment shaders driven by a fragment processor. This parallel programming strategy significantly accelerates optimization algorithms such as ICM and simulated annealing. Good acceleration are also achieved for parameter estimation procedures such as K-means and ICE. The experiments reported in this paper have been obtained with a mid-end, affordable graphics card available on the market.

• Type: Conference
• Year: 2005
• Where: ICAPR
• Authors: Pierre-Marc Jodoin, Jean-François St-Amour, Max Mignotte
• Keywords: ICAPR 2005, Markovian, Markovian Cost Functions, Unsupervised Markovian Segmentation