Iterative Possibility Distributions Refining in Pixel-based Images Classification Framework

B. Alsahwa, S. Almouahed, D. Guériot, B. Solaiman

2013

Abstract

In this study, an incremental and iterative approach for possibility distributions estimation in pixel-based images classification context is proposed. This approach is based on the use of possibilistic reasoning in order to enrich a set of samples serving for the initial estimation of possibility distributions. The use of possibilistic concepts enables an important flexibility for the integration of a context-based additional semantic knowledge source formed by pixels belonging with high certainty to different semantic classes (called possibilistic seeds), into the available knowledge encoded by possibility distributions. Once possibilistic seeds are extracted, possibility distributions are incrementally updated and refined. Synthetic images composed of two thematic classes are generated in order to evaluate the performances of the proposed approach. Initial possibility distributions are, first, obtained using a priori knowledge given in the form of learning areas delimitated by an expert. These areas serve for the estimation of the probability distributions of different thematic classes. The resulting probability density functions are then transformed into possibility distributions using Dubois-Prade’s probability-possibility transformation. The possibilistic seeds extraction process is conducted through the application of a possibilistic contextual rule using the confidence index used as an uncertainty measure.

References

  1. Bruzzone, L., Fernàndez P. D., 1999. Incremental-learning neural network for the classification of remote-sensing images. Elsevier Science, Pattern Recognition Letters 20, pp.1241-1248.
  2. Dubois, D., Prade, H., 1980.Fuzzy Sets and Systems: Theory and Applications. Academic Press, New York.
  3. Dubois, D., Prade, H., 1983. Unfair Coins and Necessity Measures: towards a possibilistic Interpretation of Histograms. Fuzzy Sets and Syst. Vol.10, pp. 15-20.
  4. Epanechnikov, V.A., 1969. Non-parametric estimation of a multivariate probability density. Theory of Probability and its Applications 14: 153-158.
  5. Hüllermeier, E., 2003. Possibilistic Instance-Based Learning. Fuzzy Set and Possibility Theory-Based Methods in Artificial Intelligence, vol. 148, pp. 335- 383.
  6. Kikuchi, Sh., Perincherry, V., 2004. Handling Uncertainty in Large Scale Systems with Certainty and Integrity. MIT Engineering Systems Symposium, Cambridge.
  7. Mouchaweh, M. S., Devillez, A., Villermain, L. G., Billaudel, P., 2002. Incremental learning in Fuzzy Pattern Matching, Elsevier Science.
  8. Tso, B., and Mather, P. M., 2009. classification methods for remotely sensed data. taylor & francis group.
  9. Zadeh, L.A., 1978. Fuzzy Sets as a Basis for a Theory of possibility. Fuzzy Sets Syst., vol. 1, PP.3-28, 1978.
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Paper Citation


in Harvard Style

Alsahwa B., Almouahed S., Guériot D. and Solaiman B. (2013). Iterative Possibility Distributions Refining in Pixel-based Images Classification Framework . In Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-8565-41-9, pages 176-181. DOI: 10.5220/0004264901760181


in Bibtex Style

@conference{icpram13,
author={B. Alsahwa and S. Almouahed and D. Guériot and B. Solaiman},
title={Iterative Possibility Distributions Refining in Pixel-based Images Classification Framework},
booktitle={Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2013},
pages={176-181},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004264901760181},
isbn={978-989-8565-41-9},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Iterative Possibility Distributions Refining in Pixel-based Images Classification Framework
SN - 978-989-8565-41-9
AU - Alsahwa B.
AU - Almouahed S.
AU - Guériot D.
AU - Solaiman B.
PY - 2013
SP - 176
EP - 181
DO - 10.5220/0004264901760181