A Simple Unsupervised Color Image Segmentation Method based on MRF-MAP
This work addresses efficiency and optimization issues in unsupervised color image segmentation for image processing applications, representing an incremental improvement.
The paper tackled the problem of low efficiency and local optima in unsupervised color image segmentation using MRF-MAP by designing a non-iterative energy function calculation method and a binary segmentation algorithm based on a tuned Lanczos eigensolver, achieving competitive performance compared to two state-of-the-art methods.
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.