Summary
In this study, an iterative contextual approach for images classification is proposed. This approach is based on the use of possibilistic reasoning in order to diffuse the possibilistic knowledge. 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. The possibilistic seeds extraction and classification process is conducted through the application of a possibilistic contextual rule using the confidence index used as an uncertainty measure. Once possibilistic seeds are extracted and classified, possibility distributions are updated and refined in order to diffuse the possibilistic knowledge. Synthetic and real images are used in order to evaluate the performances of the proposed approach.
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References
Mather, P.M.: Computer Processing of Remotely-Sensed Images: An introduction, 3rd edn. John Wiley & Sons, Chichester (2004)
Tso, B., Mather, P.M.: Classification methods for remotely sensed data. Taylor & Francis Group (2009)
Lu, D., Wang, Q.: A survey of image classification methods and techniques for improving classification performance. Int. Journal of Remote Sensing 28, 823–870 (2007)
Magnussen, S., Boudewyn, P., Wulder, M.: Contextual classification of Landsat TM images to forest inventory cover types. International Journal of Remote Sensing 25, 2421–2440 (2004)
Besag, J.: On the statistical analysis of dirty pictures. Journal of the Royal Statistical Society 48, 259–302 (1986)
Epanechnikov, V.A.: Non-parametric estimation of a multivariate probability density. Theory of Probability and its Applications 14, 153–158 (1969)
Zadeh, L.A.: Fuzzy Sets as a Basis for a Theory of possibility. Fuzzy Sets Syst. 1, 3–28 (1978)
Dubois, D., Prade, H.: When upper probabilities are possibility measures. Fuzzy Sets and Systems 49, 65–74 (1992)
Dubois, D., Prade, H.: Unfair Coins and Necessity Measures: towards a possibilistic Interpretation of Histograms. Fuzzy Sets and Syst. 10, 15–20 (1983)
Mouchaweh, S.: Semi-supervised classification method for dynamic applications. Fuzzy Sets and Systems 161, 544–563 (2010)
Kikuchi, S., Perincherry, V.: Handling Uncertainty in Large Scale Systems with Certainty and Integrity. In: MIT Engineering Systems Symposium, Cambridge (2004)
Nagao, M., Matsuyama, T.: Edge Preserving Smoothing. Computer Graphics and Image Processing 9, 394–407 (1979)
MacQueen, J.B.: Some Methods for classification and Analysis of Multivariate Observations. In: Proceedings of 5th Berkeley Symposium on Mathematical Statistics and Probability, pp. 281–297 (1967)
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Alsahwa, B., Almouahed, S., Guériot, D., Solaiman, B. (2014). Contextual Possibilistic Knowledge Diffusion for Images Classification. In: S. Choras, R. (eds) Image Processing and Communications Challenges 5. Advances in Intelligent Systems and Computing, vol 233. Springer, Heidelberg. https://doi.org/10.1007/978-3-319-01622-1_19
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DOI: https://doi.org/10.1007/978-3-319-01622-1_19
Publisher Name: Springer, Heidelberg
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