Gibbs random field models: a toolbox for spatial information extraction
Computers & Geosciences
Unsupervised Image Clustering Using the Information Bottleneck Method
Proceedings of the 24th DAGM Symposium on Pattern Recognition
Mutual information based measure for image content characterization
CAEPIA'05 Proceedings of the 11th Spanish association conference on Current Topics in Artificial Intelligence
Mutual information based measure for image content characterization
CAEPIA'05 Proceedings of the 11th Spanish association conference on Current Topics in Artificial Intelligence
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Rate distortion theory is one of the areas of information transmission theory with important applications in multimodal signal processing, as for example image processing, information bottleneck and steganalysis. This article present an image characterization method based on rate distortion analysis in the feature space. This space is coded using clustering as vector quantization (k-means). Since image information usually cannot be coded by single clusters, because there are image regions corresponding to groups of clusters, the rate and distortion are specifically defined. The rate distortion curve is analyzed, extracting specific features for implementing a database image classification system.