Conference of the Spanish Association for Artificial Intelligence

CAEPIA 2005: Current Topics in Artificial Intelligence pp 342-349

Mutual Information Based Measure for Image Content Characterization

  • Daniela Faur
  • Inge Gavat
  • Mihai Datcu
Conference paper

DOI: 10.1007/11881216_36

Volume 4177 of the book series Lecture Notes in Computer Science (LNCS)
Cite this paper as:
Faur D., Gavat I., Datcu M. (2006) Mutual Information Based Measure for Image Content Characterization. In: Marín R., Onaindía E., Bugarín A., Santos J. (eds) Current Topics in Artificial Intelligence. CAEPIA 2005. Lecture Notes in Computer Science, vol 4177. Springer, Berlin, Heidelberg

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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Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Daniela Faur
    • 1
  • Inge Gavat
    • 1
  • Mihai Datcu
    • 2
  1. 1.Politehnica University BucharestBucharestRomania
  2. 2.German Aerospace Center DLR OberpfaffenhofenGermany