IEEE Transactions on Image Processing
A robust hidden Markov Gauss mixture vector quantizer for a noisy source
IEEE Transactions on Image Processing
IEEE Transactions on Image Processing
An outlier-aware data clustering algorithm in mixture models
ICICS'09 Proceedings of the 7th international conference on Information, communications and signal processing
Parameter estimation in the spatial auto-logistic model with working independent subblocks
Computational Statistics & Data Analysis
Segmentation of retinal blood vessels using gaussian mixture models and expectation maximisation
HIS'13 Proceedings of the second international conference on Health Information Science
Moving object detection using Markov Random Field and Distributed Differential Evolution
Applied Soft Computing
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Image segmentation is an important tool in image processing and can serve as an efficient front end to sophisticated algorithms and thereby simplify subsequent processing. We develop a multiclass image segmentation method using hidden Markov Gauss mixture models (HMGMMs) and provide examples of segmentation of aerial images and textures. HMGMMs incorporate supervised learning, fitting the observation probability distribution given each class by a Gauss mixture estimated using vector quantization with a minimum discrimination information (MDI) distortion. We formulate the image segmentation problem using a maximum a posteriori criteria and find the hidden states that maximize the posterior density given the observation. We estimate both the hidden Markov parameter and hidden states using a stochastic expectation-maximization algorithm. Our results demonstrate that HMGMM provides better classification in terms of Bayes risk and spatial homogeneity of the classified objects than do several popular methods, including classification and regression trees, learning vector quantization, causal hidden Markov models (HMMs), and multiresolution HMMs. The computational load of HMGMM is similar to that of the causal HMM.