Simultaneous Parameter Estimation and Segmentation of Gibbs Random Fields Using Simulated Annealing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiple Resolution Segmentation of Textured Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Texture Segmentation Using Markov Random Field Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Maximum likelihood unsupervised textured image segmentation
CVGIP: Graphical Models and Image Processing
A review of recent texture segmentation and feature extraction techniques
CVGIP: Image Understanding
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
IEEE Transactions on Pattern Analysis and Machine Intelligence
Texture classification using wavelet transform
Pattern Recognition Letters
Double random field models for remote sensing image segmentation
Pattern Recognition Letters
Texture image segmentation using combined features from spatial and spectral distribution
Pattern Recognition Letters
Image segmentation by clustering of spatial patterns
Pattern Recognition Letters
Globally adaptive region information for automatic color-texture image segmentation
Pattern Recognition Letters
Texture classification via conditional histograms
Pattern Recognition Letters
Unsupervised deconvolution of sparse spike trains using stochasticapproximation
IEEE Transactions on Signal Processing
Segmentation of color lip images by spatial fuzzy clustering
IEEE Transactions on Fuzzy Systems
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multiresolution Gauss-Markov random field models for texture segmentation
IEEE Transactions on Image Processing
Segmentation of textured images using a multiresolution Gaussian autoregressive model
IEEE Transactions on Image Processing
The EM/MPM algorithm for segmentation of textured images: analysis and further experimental results
IEEE Transactions on Image Processing
Morphology-based multifractal estimation for texture segmentation
IEEE Transactions on Image Processing
Maximum-likelihood parameter estimation for unsupervised stochastic model-based image segmentation
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
Multiscale texture classification using dual-tree complex wavelet transform
Pattern Recognition Letters
A new image segmentation algorithm with applications to image inpainting
Computational Statistics & Data Analysis
The gray level aura matrices for textured image segmentation
Analog Integrated Circuits and Signal Processing
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The problem of textured image segmentation upon an unsupervised scheme is addressed. In the past two decades, there has been much interest in segmenting images involving complex random or structural texture patterns. However, most unsupervised segmentation techniques generally suffer from the lack of information about the correct number of texture classes. Therefore, this number is often assumed known or given a priori. On the basis of the stochastic expectation-maximization (SEM) algorithm, we try to perform a reliable segmentation without such prior information, starting from an upper bound of the number of texture classes. At a low resolution level, the image model assumes an autoregressive (AR) structure for the class-conditional random field. The SEM procedure is then applied to the set of AR features, yielding an estimate of the true number of texture classes, as well as estimates of the class-conditional AR parameters, and a coarse pre-segmentation. In a final stage, a regularization process is introduced for region formation by the way of a simple pairwise interaction model, and a finer segmentation is obtained through the maximization of posterior marginals. Some experimental results obtained by applying this method to synthetic textured and remote sensing images are presented. We also provide a comparison of our approach with some previously published methods using the same textured image database.