Abstract
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.
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Faur, D., Gavat, I., Datcu, M. (2006). Mutual Information Based Measure for Image Content Characterization. In: MarÃn, R., OnaindÃa, E., BugarÃn, A., Santos, J. (eds) Current Topics in Artificial Intelligence. CAEPIA 2005. Lecture Notes in Computer Science(), vol 4177. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11881216_36
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DOI: https://doi.org/10.1007/11881216_36
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