Stochastic Modeling of Image Content in Remote Sensing Image Archives |
Contents
Introduction 12579 | 1 |
Bayesian Information Extraction | 13 |
Remote Sensing and Image Content | 35 |
Copyright | |
12 other sections not shown
Common terms and phrases
algorithms analysis approach auto-binomial model auto-exponential AutoClass Bayes Bayesian classification Bayesian inference Bayesian networks calculate chapter characteristic characteristic classes clique clustering by melting complexity computer vision concept conditional least squares content description content-based query cover-type labels cross-entropy data mining data points database dataset defined denote depicted in Fig describe dissertation energy function entropy ETH Zürich ETHZ example Gabor filters Gaussian Geman Gibbs random fields given GLCM hierarchical hyper-parameters image classification image content image data information extraction information systems interface Jain Landsat Landsat TM likelihood Markov random field matrix maximum a posteriori MMDEMO model selection 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 retrieval random field models remote sensing image robust segmentation sensor signal classes spatial spectral stochastic modeling techniques texture features texture model tion un-supervised clustering user interests visual