One-Class Support Vector Ensembles for Image Segmentation and Classification

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.