Minimum loss of information and image segmentation

  • Bruno Forte
  • Vladimir Kolbas
6. Information
Part of the Lecture Notes in Computer Science book series (LNCS, volume 521)


A new measure of the information loss in image segmentation is derived from a set of natural properties. A similar quantity can be used in the quantization of a continuous real random n-vector. A new method for thresholding the grey-level histogram of a picture is then introduced. The method is based on the natural requirement of minimum information loss.


quantization image segmentation information loss entropy 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. Abutaleb, A.S. (1989) “Automatic Thresholding Grey-Level Pictures Using Two-Dimensional Entropy”, Comput. Vision, Graphics Image Process., 42, pp. 22–31.Google Scholar
  2. Aczél, J. (1978) “A mixed Theory of Information — II: Additive Inset Entropies (of Randomized systems of Events) with Measurable Sum Property”, Utilitas Mathematica, Vol. 13, pp. 49–54.Google Scholar
  3. Aczél, J. and Daroczy, Z. (1975) “On measures of Information and their Characterizations”, Academic Press, New York-San Francisco-London.Google Scholar
  4. Caselli, R. and Forte, B. (1988) “Thresholding Grey-Level Histograms by Minimum Information Loss”, SASIAM internal report.Google Scholar
  5. Ebanks, B.R. (1990) “Branching inset entropies on open domains”, Aequationes Mathematicae, 39, pp. 100–113.CrossRefGoogle Scholar
  6. Forte, B. and Kolbas, V. “Some Experimental Results in Image Segmentation by Minimum Loss of Information”, manuscript.Google Scholar
  7. Forte, B., Ng, C.T. and Lo Schiavo, M. (1984) “Additive and Subadditive Entropies for Discrete Random Vectors”, Journal of Comb. Info. and Systems Sc., Vol. 9, No. 4, 207–216.Google Scholar
  8. Forte, B. and Sahoo, P.K., “Minimal Loss of Information and Optimal Thresholds for Digital Images”, manuscript.Google Scholar
  9. Sahoo, P.K., Soltani, S., Wong, A.K.C. and Chen, Y.C. (1988) “A Survey of Thresholding Techniques”, Computer Vision, Graphics and Image Process., 41, 233–260.Google Scholar
  10. Wong, A.K.C. and Sahoo, P.K. (1989) “A Grey-Level Threshold Selection Method Based on Maximum Entropy Principle”, IEEE Trans. on Systems, Man and Cybernetics, Vol. 19, No. 4, July/August 866–871.Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 1991

Authors and Affiliations

  • Bruno Forte
    • 1
  • Vladimir Kolbas
    • 1
  1. 1.Department of Applied MathematicsUniversity of WaterlooWaterlooCanada

Personalised recommendations