Current Topics in Artificial Intelligence

Volume 4177 of the series Lecture Notes in Computer Science pp 342-349

Mutual Information Based Measure for Image Content Characterization

  • Daniela FaurAffiliated withPolitehnica University Bucharest
  • , Inge GavatAffiliated withPolitehnica University Bucharest
  • , Mihai DatcuAffiliated withGerman Aerospace Center DLR Oberpfaffenhofen

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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.