Abstract
We propose a method of real-time implementation of an approximation of the support vector machine decision rule. The method uses an improvement of a supervised classification method based on hyperrectangles, which is useful for real-time image segmentation. We increase the classification and speed performances using a combination of classification methods: a support vector machine is used during a pre-processing step. We recall the principles of the classification methods and we evaluate the hardware implementation cost of each method. We present our learning step combination algorithm and results obtained using Gaussian distributions and an example of image segmentation coming from a part of an industrial inspection problem The results are evaluated regarding hardware cost as well as classification performances.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Bishop, C.M.: Neural networks for Pattern Recognition, Oxford University Press, (1995) 110–230.
Dubuisson, B.: Diagnostic et reconnaissance des formes, HERMES, Paris, (1990).
Chapman, K.: Constant coefficient multipliers for the XC4000E. Xilinx Application, Note XAPP054, Xilinx, Inc (1996).
Duda, R. O., Hart, P.E.: Pattern classification and scene analysis, Wiley, New York, (1973) 230–243.
Enzler, R., Jeger, T. Cottet, D., and Tröster, G.: High-Level Area and Performance Estimation of Hardware Building Blocks on FPGAs, In Field-Programmable Logic and Applications (Proc. FPL 00), Lecture Notes in Computer Science, Vol. 1896, Springer, (2000) 525–534
Hearst, M. A., Schölkopf, B., Dumais, S., Osuna, E., Platt J.: Trends and Controversies-Support Vector Machines. IEEE Intelligent Systems, Vol. 13(4), (1998) 18–28.
Jonsson, K., Kittler J., P. Li Y., Matas, J.: Support Vector Machines for Face Authentication. In T. Pridmore and D. Elliman, editors, British Machine Vision Conference, (1999) 543–553.
Hauck, S.: The Roles of FPGAs in Reprogrammable Systems, Proceedings of the IEEE, Vol. 86(4), (1998) 615–638.
Kittler, J., Hatef, M., Duin, R. P. W., Matas, J.: On combining classifiers in IEEE transactions on pattern analysis and machine intelligence, Vol. 20(3) (1998), 226–239.
Kittler, J.: Feature set search algorithms, Pattern recognition and signal processing, Sijthoff and Noordhoff, Alphen aan den Rijn, Netherlands, (1978) 41–60.
Miteran, J., Geveaux, P., Bailly, R. and Gorria, P.: Real-time defect detection using image segmentation Proceedings of IEEE-ISIE 97, Guimares, Portugal, (1997) 713–716.
Miteran, J., Gorria, P., Robert, M.: Classification géométrique par polytopes de contraintes. Performances et intégration, Traitement du Signal, Vol 11 (1994) 393–408.
Miteran, J., Zimmer, J. P., Yang, F. Paindavoine M.: Access control: adaptation and realtime implantation of a face recognition method, Optical Engineering, 40(4), (2001) 586–593.
Moobed, B.: Combinaison de classifieurs, une nouvelle approche, Phd. thesis, Laboratoire d’informatique de polytechnique d’Orsay; France (1996).
Niyogi, P., Burges, C., Ramesh P.: Distinctive Feature Detection Using Support Vector Machines, ICASSP 99, 1, (1999) 425–428.
Robert, M., Gorria, P., Mitéran, J., Turgis, S.: Architectures for real-time classification processor, Custom Integrated Circuit Conference, San Diego CA, (1994) 197–200.
Salzberg S.: A nearest hyperrectangle learning method. Machine Learning, Vol. 6 (1991), 251–276.
Schölkopf, B., Smola, A., Müller, K.-R. Burges, C. J. C., Vapnik V.: Support Vector methods in learning and feature extraction, Australian Journal of Intelligent Information Processing Systems, Vol 1, (1998) 3–9.
Somol, P,. Pudil, P., Novovocova, J. Paclik, P.: Adaptative floating search methods in feature selection, Pattern Recognition Letters, Vol. 20, (1999) 1157–1163.
Vapnik, V.: The nature of statistical learning theory, Springer-Verlag New York (1995).
Wettschereck, D., Dietterich, T.: An Experimental Comparison of the Nearest-Neighbor and Nearest-Hyperrectangle Algorithms, Machine Learning, Vol. 19(1), (1995) 5–27
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2003 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Mitéran, J., Bouillant, S., Bourennane, E. (2003). Classification Boundary Approximation by Using Combination of Training Steps for Real-Time Image Segmentation. In: Perner, P., Rosenfeld, A. (eds) Machine Learning and Data Mining in Pattern Recognition. MLDM 2003. Lecture Notes in Computer Science, vol 2734. Springer, Berlin, Heidelberg. https://doi.org/10.1007/3-540-45065-3_13
Download citation
DOI: https://doi.org/10.1007/3-540-45065-3_13
Published:
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-40504-7
Online ISBN: 978-3-540-45065-8
eBook Packages: Springer Book Archive