A Simple Unsupervised Color Image Segmentation Method based on MRF-MAP

TL;DR

This paper introduces a fast, non-iterative unsupervised color image segmentation method based on MRF-MAP, improving efficiency and accuracy over traditional iterative approaches.

Contribution

It proposes a novel non-iterative algorithm using a tuned Lanczos eigensolver for MRF-MAP-based segmentation, addressing efficiency and local optima issues.

Findings

Achieves competitive segmentation performance with state-of-the-art methods.

Significantly improves efficiency over traditional iterative MRF-MAP methods.

Reduces risk of local optima trapping in unsupervised segmentation.

Abstract

Color image segmentation is an important topic in the image processing field. MRF-MAP is often adopted in the unsupervised segmentation methods, but their performance are far behind recent interactive segmentation tools supervised by user inputs. Furthermore, the existing related unsupervised methods also suffer from the low efficiency, and high risk of being trapped in the local optima, because MRF-MAP is currently solved by iterative frameworks with inaccurate initial color distribution models. To address these problems, the letter designs an efficient method to calculate the energy functions approximately in the non-iteration style, and proposes a new binary segmentation algorithm based on the slightly tuned Lanczos eigensolver. The experiments demonstrate that the new algorithm achieves competitive performance compared with two state-of-art segmentation methods.