Unsupervised Learning of Finite Mixture Models
IEEE Transactions on Pattern Analysis and Machine Intelligence
Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood
IEEE Transactions on Pattern Analysis and Machine Intelligence
Nonparametric Bayesian Image Segmentation
International Journal of Computer Vision
A Bayesian framework for image segmentation with spatially varying mixtures
IEEE Transactions on Image Processing
Logistic Stick-Breaking Process
The Journal of Machine Learning Research
Smooth image segmentation by nonparametric bayesian inference
ECCV'06 Proceedings of the 9th European conference on Computer Vision - Volume Part I
Bayesian image segmentation using gaussian field priors
EMMCVPR'05 Proceedings of the 5th international conference on Energy Minimization Methods in Computer Vision and Pattern Recognition
IEEE Transactions on Image Processing
Image segmentation and labeling using the Polya urn model
IEEE Transactions on Image Processing
A spatially constrained mixture model for image segmentation
IEEE Transactions on Neural Networks
A Spatially Constrained Generative Model and an EM Algorithm for Image Segmentation
IEEE Transactions on Neural Networks
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We propose an image prior for the model-based nonparametric classification of synthetic aperture radar (SAR) images that allows working with infinite number of mixture components. In order to enclose the spatial interactions of the pixel labels, the prior is derived by incorporating a conditional multinomial auto-logistic random field into the Normalized Gamma Process prior. In this way, we obtain an image classification prior that is free from the limitation on the number of classes and includes the smoothing constraint into classification problem. In this model, we introduced a hyper-parameter that can control the preservation of the important classes and the extinction of the weak ones. The recall rates reported on the synthetic and the real TerraSAR-X images show that the proposed model is capable of accurately classifying the pixels. Unlike the existing methods, it applies a simple iterative update scheme without performing a hierarchical clustering strategy. We demonstrate that the estimation accuracy of the proposed method in number of classes outperforms the conventional finite mixture models.