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.
Similar content being viewed by others
References
Barla, A., Franceschi, E., Odone, F., Verri, A.: Image Kernels. Lecture Notes in Computer Science, vol. 2388, pp. 83–96. Springer, Berlin (2002)
Barandiarán, I., Paloc, C., Graña, M.: Real-time optical markerless tracking for augmented reality applications. J. Real-Time Image Process. 5(2), 129–138 (2010)
Ben-Hur, A., Horn, D., Siegelmann, H.T., Vapnik, V.: Support vector clustering. J. Mach. Learn. Res. 2, 125–137 (2001)
Ben-Hur, A., Elisseeff, A., Guyon, I.: A stability based method for discovering structure in cluster data. In: Proceedings Pacific Symposium on Biocomputing, pp. 6–17 (2002)
The Berkeley Segmentation Database (http://www.eecs.berkeley.edu/Research/Projects/CS/vision/grouping/segbench/) (2010)
Bezdek, J.: Pattern Recognition with Fuzzy Objective Function Algorithms. Plenum, New York (1981)
Bertsekas, D.P.: Constraint Optimization and Lagrange Multiplier Methods. Athena Scientific (1996)
Bicego, M., Figueiredo, M.A.T.: Soft clustering using weighted one-class support vector machines. Pattern Recognit. 42, 27–32 (2009)
Blake, C., Keogh, E., Merz, C.: UCI repository of machine learning databases. University of California, Irvine, Department of Information and Computer Science (www.ics.uci.edu/~mlearn/MLRepository.html) (1998)
Camastra, F., Verri, A.: A novel kernel method for clustering. IEEE Trans. Pattern Anal. Mach. Intell. 27, 801–805 (2005)
Chang, C.-C., Lin, C.-J.: LIBSVM, a library for support vector machines (www.csie.ntu.edu.tw/~cjlin/libsvm) (2001)
Cyganek, B.: Framework for object tracking with support vector machines, structural tensor and the mean shift method. In: Leung, C.S., Lee, M., Chan, J.H. (eds.) ICONIP 2009: 16th International Conference on Neural Information Processing, Part I. Bangkok, Thailand, 1–5 December 2009. Lecture Notes in Computer Science, vol. 5863, pp. 399–408. Springer, Berlin (2009)
Cyganek, B., Siebert, J.P.: An Introduction to 3D Computer Vision Techniques and Algorithms. Wiley, New York (2009)
Cyganek, B.: Image segmentation with a hybrid ensemble of one-class support vector machines. In: Graña Romay, M. et al. (eds.) The International Conference on Hybrid Artificial Intelligence Systems, San Sebastian, Spain, HAIS 2010, Part I, Lecture Notes in Artificial Intelligence, vol. 6076, pp. 256–263. Springer, Berlin (2010)
Cyganek, B.: http://home.agh.edu.pl/~cyganek/OCSVMEnsemble.zip (2011)
Duda, R.O., Hart, P.E., Stork, D.G.: Pattern Classification. Wiley, New York (2001)
Filipponea, M., Camastra, F., Masullia, F., Rovetta, S.: A survey of kernel and spectral methods for clustering. Pattern Recognit. 41, 176–190 (2008)
Fletcher, R.: Practical Methods of Optimization, 2nd edn. Wiley, New York (2003)
Frigui, H., Krishnapuram, R.: Clustering by competitive agglomeration. Pattern Recognit. 30(7), 1109–1119 (1997)
Frigui, H.: Simultaneous clustering and feature discrimination with applications. In: de Oliveira, J.V., Pedrycz, W. (eds.) Advances in Fuzzy Clustering and its Applications, pp. 285–312. Wiley, New York (2007)
Gestel, T.V., Suykens, J.A.K., Baesens, B., Viaene, S., Vanthienen, J., Dedene, G., De Moor, B., Vandewalle, J.: Benchmarking least squares support vector machine classifiers. Mach. Learn. 54(1), 5–32 (2004)
Hsu, C.-W., Chang, C.-C., Lin, C.-J.: A practical guide to support vector classification. Department of Computer Science and Information Engineering, National Taiwan University (www.csie.ntu.edu.tw/~cjlin/papers/guide/guide.pdf) (2003)
Jackowski, K., Woźniak, M.: Algorithm of designing compound recognition system on the basis of combining classifiers with simultaneous splitting feature space into competence areas. Pattern Anal. Appl. 12, 415–425 (2009)
Kittler, J., Hatef, M., Duing, R.P.W., Matas, J.: On combining classifiers. IEEE Trans. Pattern Anal. Mach. Intell. 20(3), 226–239 (1998)
Kruse, R., Döring, C., Lesot, M.-J.: Fundamentals of fuzzy clustering. In: de Oliveira, J.V., Pedrycz, W. (eds.) Advances in Fuzzy Clustering and Its Applications, pp. 3–30. Wiley, New York (2007)
Kuncheva, L.I.: Cluster-and-selection method for classifier combination. In: Proc. 4th International Conference on Knowledge-Based Intelligent Engineering Systems & Allied Technologies (KES’2000), Brighton, UK, pp. 185–188 (2000)
Kuncheva, L.I.: Combining Pattern Classifiers. Wiley, New York (2004)
Martin, D., Fowlkes, C., Tal, D., Malik, J.: A database of human segmented natural images and its application to evaluating segmentation algorithms and measuring ecological statistics. In: Proc. 8th Int’l Conf. Computer Vision, pp. 416–423 (2001)
Moya, M., Koch, M., Hostetler, L.: One-class classifier networks for target recognition applications. In: Proceedings World Congress on Neural Networks. International Neural Network Society INNS, pp. 797–801 (1993)
Odone, F., Barla, A., Verri, A.: Building kernels from binary strings for image matching. IEEE Trans. Image Process. 14(2), 169–180 (2005)
Polikar, R.: Ensemble based systems in decision making. IEEE Circuits and Systems Magazine. pp. 21–45 (2006)
Press, W.H., Teukolsky, S.A., Vetterling, W.T., Flannery, B.P.: Numerical Recipes in C++. The Art of Scientific Computing, 3rd edn. Cambridge University Press, Cambridge (2007)
Ritter, G., Gallegos, M.: Outliers in statistical pattern recognition and an application to automatic chromosome classification. Pattern Recognit. Lett. 18, 525–539 (1997)
Rüping, S.: mySVM – Manual. AI Unit University of Dortmund, Computer Science Department (2000)
Schölkopf, B., Smola, A.J.: Learning with Kernels. MIT Press, Cambridge (2002)
Shawe-Taylor, J., Cristianini, N.: Kernel Methods for Pattern Analysis. Cambridge University Press, Cambridge (2004)
Tax, D.M.J.: One-class classification. PhD thesis, TU Delft University (2001)
Tax, D., Duin, R.: Support vector domain description. Pattern Recognit. Lett. 20, 1191–1199 (1999)
Tax, D., Duin, R.: Support vector data description. Mach. Learn. 54, 45–66 (2004)
University of California, Database (ftp://ftp.ics.uci.edu/pub/machine-learning-databases/) (2011)
Vapnik, V.N.: The Nature of Statistical Learning Theory. Springer, Berlin (1995)
Wu, K.-L., Yang, M.-S.: Alternative c-means clustering algorithms. Pattern Recognit. 35, 2267–2278 (2002)
Wu, Z., Xie, W., Yu, J.: Fuzzy C-means clustering algorithm based on kernel method. In: Fifth International Conference on Computational Intelligence and Multimedia Applications (ICCIMA’03), pp. 1–6 (2003)
Zhang, D., Chen, S.: Clustering incomplete data using kernel-based fuzzy c-means algorithm. Neural Process. Lett. 18(3), 155–162 (2003)
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
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
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10851-011-0304-0