Abstract
One objective for classifying textures in natural images is to achieve the best performance possible. As reported in the literature, the combination of classifiers performs better than simple ones. The problem is how they can be combined. We propose a relaxation approach, which combines two base classifiers, namely: the probabilistic Bayesian and the fuzzy clustering. The first establishes an initial classification, where the probability values are reinforced or punished by relaxation based on the support provided by the second. A comparative analysis is carried out against classical classifiers, verifying its performance.
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Guijarro, M., Pajares, G., Herrera, P.J. (2008). On Combining Classifiers by Relaxation for Natural Textures in Images. In: Corchado, E., Abraham, A., Pedrycz, W. (eds) Hybrid Artificial Intelligence Systems. HAIS 2008. Lecture Notes in Computer Science(), vol 5271. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-87656-4_43
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DOI: https://doi.org/10.1007/978-3-540-87656-4_43
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