Possibilistic Similarity based Image Classification

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


In this study, an approach for image classification based on possibilistic similarity is proposed. This approach, due to the use of possibilistic concepts, enables an important flexibility to integrate both contextual information and a priori knowledge. 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 density functions of different thematic classes. The resulting probability density functions are then transformed into possibility distributions using Dubois-Prade’s probability-possibility transformation. Several measures of similarity between classes were tested in order to improve the discrimination between classes. The classification is then performed based on the principle of possibilistic similarity. Synthetic and real images are used in order to evaluate the performances of the proposed model.


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Paper Citation

in Harvard Style

Alsahwa B., Almouahed S., Guériot D. and Solaiman B. (2013). Possibilistic Similarity based Image Classification . In Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-8565-41-9, pages 271-275. DOI: 10.5220/0004265002710275

in Bibtex Style

author={B. Alsahwa and S. Almouahed and D. Guériot and B. Solaiman},
title={Possibilistic Similarity based Image Classification},
booktitle={Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},

in EndNote Style

JO - Proceedings of the 2nd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Possibilistic Similarity based Image Classification
SN - 978-989-8565-41-9
AU - Alsahwa B.
AU - Almouahed S.
AU - Guériot D.
AU - Solaiman B.
PY - 2013
SP - 271
EP - 275
DO - 10.5220/0004265002710275