Cross—validation and the smoothing of orthogonal series density estimators
Journal of Multivariate Analysis
Adaptive mixture estimation and unsupervised local Bayesian image segmentation
Graphical Models and Image Processing
Evaluation and comparison of different segmentation algorithms
Pattern Recognition Letters
Bayesian learning, global competition and unsupervised image segmentation
Pattern Recognition Letters
Optimization by Vector Space Methods
Optimization by Vector Space Methods
An unsupervised and non-parametric Bayesian classifier
Pattern Recognition Letters
Estimation of fuzzy Gaussian mixture and unsupervised statistical image segmentation
IEEE Transactions on Image Processing
Maximum-likelihood parameter estimation for unsupervised stochastic model-based image segmentation
IEEE Transactions on Image Processing
GPU-accelerated MRF segmentation algorithm for SAR images
Computers & Geosciences
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This paper deals with the problem of unsupervised image segmentation which consists in first mixture identification phase and second a Bayesian decision phase. During the mixture identification phase, the conditional probability density function (pdf) and the a priori class probabilities must be estimated. The most difficult part is the estimation of the number of pixel classes or in other words the estimation of the number of density mixture components. To resolve this problem, we propose here a Stochastic and Nonparametric Expectation-Maximization (SNEM) algorithm. The algorithm finds the most likely number of classes, their associated model parameters and generates a segmentation of the image by classifying the pixels into these classes. The non-parametric aspect comes from the use of the orthogonal series estimator. Experimental results are promising, we have obtained accurate results on a variety of real images.