Estimation of Generalized Multisensor Hidden Markov Chains and Unsupervised Image Segmentation
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised Segmentation of Color-Texture Regions in Images and Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Sequential combination methods for data clustering analysis
Journal of Computer Science and Technology
An unsupervised and non-parametric Bayesian classifier
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
A Statistical Model for Contours in Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Unsupervised image segmentation using triplet Markov fields
Computer Vision and Image Understanding
Unsupervised color-texture segmentation based on soft criterion with adaptive mean-shift clustering
Pattern Recognition Letters
Unsupervised Statistical Segmentation of Nonstationary Images Using Triplet Markov Fields
IEEE Transactions on Pattern Analysis and Machine Intelligence
KPCA for semantic object extraction in images
Pattern Recognition
Pearson-based mixture model for color object tracking
Machine Vision and Applications
optimization-based image segmentation by genetic algorithms
Journal on Image and Video Processing - Regular
Unsupervised image segmentation using triplet Markov fields
Computer Vision and Image Understanding
Segmentation-driven image fusion based on alpha-stable modeling of wavelet coefficients
IEEE Transactions on Multimedia
Color-texture image segmentation by combining region and photometric invariant edge information
MCAM'07 Proceedings of the 2007 international conference on Multimedia content analysis and mining
A hybrid segmentation method applied to color images and 3d information
MICAI'06 Proceedings of the 5th Mexican international conference on Artificial Intelligence
Unsupervised color image segmentation using mean shift and deterministic annealing EM
ICCSA'05 Proceedings of the 2005 international conference on Computational Science and Its Applications - Volume Part IV
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We introduce the notion of a generalized mixture and propose some methods for estimating it, along with applications to unsupervised statistical image segmentation. A distribution mixture is said to be “generalized” when the exact nature of the components is not known, but each belongs to a finite known set of families of distributions. For instance, we can consider a mixture of three distributions, each being exponential or Gaussian. The problem of estimating such a mixture contains thus a new difficulty: we have to label each of three components (there are eight possibilities). We show that the classical mixture estimation algorithms-expectation-maximization (EM), stochastic EM (SEM), and iterative conditional estimation (ICE)-can be adapted to such situations once as we dispose of a method of recognition of each component separately. That is, when we know that a sample proceeds from one family of the set considered, we have a decision rule for what family it belongs to. Considering the Pearson system, which is a set of eight families, the decision rule above is defined by the use of “skewness” and “kurtosis”. The different algorithms so obtained are then applied to the problem of unsupervised Bayesian image segmentation, We propose the adaptive versions of SEM, EM, and ICE in the case of “blind”, i.e., “pixel by pixel”, segmentation. “Global” segmentation methods require modeling by hidden random Markov fields, and we propose adaptations of two traditional parameter estimation algorithms: Gibbsian EM (GEM) and ICE allowing the estimation of generalized mixtures corresponding to Pearson's system. The efficiency of different methods is compared via numerical studies, and the results of unsupervised segmentation of three real radar images by different methods are presented