Abstract
In this paper an efficient method of image segmentation from large data samples is presented. Segmentation is stated as a novelty detection problem for which the one-class support vector machines (OC-SVM) are employed. However, to improve performance and scalability the input space of samples is first k-means partitioned, and then each partition is independently trained with an OC-SVM. This way a parallel structure of expert classifiers is obtained with of a small average number of support vectors and high precision.
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Cyganek, B. (2010). Image Segmentation with a Hybrid Ensemble of One-Class Support Vector Machines. In: Graña Romay, M., Corchado, E., Garcia Sebastian, M.T. (eds) Hybrid Artificial Intelligence Systems. HAIS 2010. Lecture Notes in Computer Science(), vol 6076. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-13769-3_31
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DOI: https://doi.org/10.1007/978-3-642-13769-3_31
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-642-13768-6
Online ISBN: 978-3-642-13769-3
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