Gibbs random field models: a toolbox for spatial information extraction
Computers & Geosciences
Content-Based Image Retrieval at the End of the Early Years
IEEE Transactions on Pattern Analysis and Machine Intelligence
Digital Image Processing
Image disorder characterization based on rate distortion
CAEPIA'05 Proceedings of the 11th Spanish association conference on Current Topics in Artificial Intelligence
A method for measuring the complexity of image databases
IEEE Transactions on Multimedia
Semantic Map Generation from Satellite Images for Humanitarian Scenarios Applications
ACIVS '08 Proceedings of the 10th International Conference on Advanced Concepts for Intelligent Vision Systems
Image disorder characterization based on rate distortion
CAEPIA'05 Proceedings of the 11th Spanish association conference on Current Topics in Artificial Intelligence
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An image can be decomposed into different elementary descriptors depending on the observer interest. Similar techniques as used to understand words, regarded as molecules, formed by combining atoms, are proposed to describe images based on their information content. In this paper, we use primitive feature extraction and clustering to code the image information content. Our purpose is to describe the complexity of the information based on the combinational profile of the clustered primitive features using entropic measures like mutual information and Kullback-Leibler divergence. The developed method is demonstrated to asses image complexity for further applications to improve Earth Observation image analysis for sustainable humanitarian crisis response in risk reduction.