The theory and practice of Bayesian image labeling
International Journal of Computer Vision
Introduction to statistical pattern recognition (2nd ed.)
Introduction to statistical pattern recognition (2nd ed.)
A game strategy approach for image labeling
Computer Vision and Image Understanding
Optimal combinations of pattern classifiers
Pattern Recognition Letters
IEEE Transactions on Pattern Analysis and Machine Intelligence
On combining classifiers using sum and product rules
Pattern Recognition Letters
Multidimensional pattern recognition problems and combining classifiers
Pattern Recognition Letters
Double random field models for remote sensing image segmentation
Pattern Recognition Letters
Combining Pattern Classifiers: Methods and Algorithms
Combining Pattern Classifiers: Methods and Algorithms
Flexible nonlinear contextual classification
Pattern Recognition Letters - Special issue: Pattern recognition for remote sensing (PRRS 2002)
Pattern Recognition Letters - Special issue: Artificial neural networks in pattern recognition
Pattern Recognition, Third Edition
Pattern Recognition, Third Edition
Adaptive combination of adaptive classifiers for handwritten character recognition
Pattern Recognition Letters
Classifier ensemble selection using hybrid genetic algorithms
Pattern Recognition Letters
Pattern Recognition Letters
MAP-MRF super-resolution image reconstruction using maximum pseudo-likelihood parameter estimation
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Maximum pseudo likelihood estimation in network tomography
IEEE Transactions on Signal Processing
Stochastic Relaxation, Gibbs Distributions, and the Bayesian Restoration of Images
IEEE Transactions on Pattern Analysis and Machine Intelligence
Inversion of large-support ill-posed linear operators using a piecewise Gaussian MRF
IEEE Transactions on Image Processing
A Segmentation Model Using Compound Markov Random Fields Based on a Boundary Model
IEEE Transactions on Image Processing
CIARP'11 Proceedings of the 16th Iberoamerican Congress conference on Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications
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In this paper we present a novel approach for multispectral image contextual classification by combining iterative combinatorial optimization algorithms. The pixel-wise decision rule is defined using a Bayesian approach to combine two MRF models: a Gaussian Markov Random Field (GMRF) for the observations (likelihood) and a Potts model for the a priori knowledge, to regularize the solution in the presence of noisy data. Hence, the classification problem is stated according to a Maximum a Posteriori (MAP) framework. In order to approximate the MAP solution we apply several combinatorial optimization methods using multiple simultaneous initializations, making the solution less sensitive to the initial conditions and reducing both computational cost and time in comparison to Simulated Annealing, often unfeasible in many real image processing applications. Markov Random Field model parameters are estimated by Maximum Pseudo-Likelihood (MPL) approach, avoiding manual adjustments in the choice of the regularization parameters. Asymptotic evaluations assess the accuracy of the proposed parameter estimation procedure. To test and evaluate the proposed classification method, we adopt metrics for quantitative performance assessment (Cohen's Kappa coefficient), allowing a robust and accurate statistical analysis. The obtained results clearly show that combining sub-optimal contextual algorithms significantly improves the classification performance, indicating the effectiveness of the proposed methodology.