Efficient Parallel Estimation for Markov Random Fields

TL;DR

This paper introduces a deterministic distributed MAP estimation algorithm for Markov Random Fields, demonstrating superior segmentation results and efficiency over stochastic methods in computer vision tasks.

Contribution

The paper presents Local Highest Confidence First (Local HCF), a novel deterministic distributed algorithm for MAP estimation in Markov Random Fields, improving accuracy and computational efficiency.

Findings

Local HCF outperforms stochastic algorithms in segmentation accuracy.

Local HCF requires significantly less computation.

Experiments validate the effectiveness of the proposed method.

Abstract

We present a new, deterministic, distributed MAP estimation algorithm for Markov Random Fields called Local Highest Confidence First (Local HCF). The algorithm has been applied to segmentation problems in computer vision and its performance compared with stochastic algorithms. The experiments show that Local HCF finds better estimates than stochastic algorithms with much less computation.