Abstract
One objective for classifying textures in natural images is to achieve the best performance possible. Unsupervised techniques are suitable when no prior knowledge about the image content is available. The main drawback of unsupervised approaches is its worst performance as compared against supervised ones. We propose a new unsupervised hybrid approach based on two well-tested classifiers: Vector Quantization (VQ) and Fuzzy k-Means (FkM). The VQ unsupervised methods establishes an initial partition which is validated and improved through the supervised FkM. A comparative analysis is carried out against classical classifiers, verifying its performance.
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Guijarro, M., Abreu, R., Pajares, G. (2007). A New Unsupervised Hybrid Classifier for Natural Textures in Images. In: Corchado, E., Corchado, J.M., Abraham, A. (eds) Innovations in Hybrid Intelligent Systems. Advances in Soft Computing, vol 44. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74972-1_37
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DOI: https://doi.org/10.1007/978-3-540-74972-1_37
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-74971-4
Online ISBN: 978-3-540-74972-1
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