Stochastic Modeling of Image Content in Remote Sensing Image Archives |
Contents
Introduction | 1 |
Hierarchical Bayesian Modeling of Image Content | 4 |
Bayesian Information Extraction | 13 |
Copyright | |
13 other sections not shown
Common terms and phrases
algorithms analysis approach arg max auto-binomial model AutoClass Bayes Bayesian classification Bayesian inference Bayesian networks calculate chapter characteristic clique clustering by melting complexity computer vision content-based query cover-type labels cross-entropy data mining data points database dataset defined denote depicted in Fig describe dissertation energy function entropy example features based frequentist Gabor filters Gaussian Geman Gibbs random fields given GLCM hierarchical image archive image classification image content image data information extraction K-means Landsat Landsat TM likelihood Markov random field matrix maximum a posteriori MMDEMO model selection number of classes number of clusters obtain Occam factor optimum parameter space parameter vector parametric modeling particular pixel pixel values posterior map posterior probability prior prior probability probabilistic random field models Rehrauer remote sensing image retrieval robust segmentation sensor signal classes spatial spectral stochastic modeling structures techniques texture features texture model tion un-supervised clustering user interests user-specific visual