Image segmentation based on maximum-likelihood estimation and optimum entropy-distribution (MLE–OED)

Abstract

A novel method based on MLE–OED is proposed for unsupervised image segmentation of multiple objects with fuzzy edges. It adjusts the parameters of a mixture of Gaussian distributions via minimizing a new loss function. The loss function consists of two terms: a local content fitting term, which optimizes the entropy distribution, and a global statistical fitting term, which maximizes the likelihood of the parameters for the given data. The proposed segmentation method is validated by experiments on both synthetic and real images. The experimental results show that the proposed method outperformed two popular methods.