Abstract
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.
Highlights
Image segmentation is one of the major challenges in image processing and computer vision
We present the results of the application of the Adaptive Distance EM (ADEM) algorithm
Concerning the spatial attributs, the ADEM algorithm uses lthe average calculated on a window of size 3x3 analysis
Summary
Image segmentation is one of the major challenges in image processing and computer vision. The Markov Random Field (MRF) models were used with images in an important number of works to add spatial smoothness into the process of image segmentation [11,13,15]. This approach provide satisfactory results in many case, but most case the assumption of a single Gaussian distribution typically limits image segmentation accuracy. The segmentation algorithm developed in this paper is based on a parametric model in which the probability density function of the gray levels in the image is a mixture of Gaussian density functions. The model-based segmentation algorithms not allows a good results if the histogram of an image is a poor Gaussian approximation. The application of this model in image segmentation is, limited to the images which are a good approximations of Gaussian mixtures with well-defined modes
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