Efficient Parallel Estimation for Markov Random Fields
This work addresses the need for efficient and accurate segmentation in computer vision, though it appears incremental as it builds on existing HCF methods.
The authors tackled the problem of MAP estimation in Markov Random Fields by introducing Local HCF, a deterministic distributed algorithm that outperforms stochastic methods in segmentation tasks with significantly reduced computational cost.
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.