Image segmentation by adaptive distance based on EM algorithm
This is an incremental improvement for image segmentation tasks, addressing noise sensitivity in a specific domain.
The paper tackles the problem of noise sensitivity in image segmentation using finite Gaussian mixture models by introducing an adaptive distance metric that weights pixel features and neighborhood characteristics differently based on spatial position. The results show a significant improvement over the standard EM algorithm.
This paper introduces a Bayesian image segmentation algorithm based on finite mixtures. An EM algorithm is developed to estimate parameters of the Gaussian mixtures. The finite mixture is a flexible and powerful probabilistic modeling tool. It can be used to provide a model-based clustering in the field of pattern recognition. However, the application of finite mixtures to image segmentation presents some difficulties; especially it's sensible to noise. In this paper we propose a variant of this method which aims to resolve this problem. Our approach proceeds by the characterization of pixels by two features: the first one describes the intrinsic properties of the pixel and the second characterizes the neighborhood of pixel. Then the classification is made on the base on adaptive distance which privileges the one or the other features according to the spatial position of the pixel in the image. The obtained results have shown a significant improvement of our approach compared to the standard version of EM algorithm.