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One-Class Support Vector Ensembles for Image Segmentation and Classification

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Abstract

This paper presents an extension of the one-class support vector machines (OC-SVM) into an ensemble of soft OC-SVM classifiers. The idea consists in prior clustering of the input data with a kernel version of the deterministically annealed fuzzy c-means. This way partitioned data is trained with a number of soft OC-SVM classifiers which allow weight assignment to each of the training data. Weights are obtained from the cluster membership values, computed in the kernel fuzzy c-means. The method was designed and tested mostly in the tasks of image classification and segmentation, although it can be used for other one-class problems.

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  1. AGH University of Science and Technology, Al. Mickiewicza 30, 30-059, Kraków, Poland

    Bogusław Cyganek

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  1. Bogusław Cyganek
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Correspondence to Bogusław Cyganek.

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Cyganek, B. One-Class Support Vector Ensembles for Image Segmentation and Classification. J Math Imaging Vis 42, 103–117 (2012). https://doi.org/10.1007/s10851-011-0304-0

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