SVM approximation for real-time image segmentation by using an improved hyperrectangles-based method

Authors:
J. Mitéran;S. Bouillant;E. Bourennane
Affiliations:
Laboratoire Le2i, UMR CNRS 5158 Aile des Sciences de l'ingénieur, Université de Bourgogne, BP 47870, 21078 Dijon, France;Laboratoire Le2i, UMR CNRS 5158 Aile des Sciences de l'ingénieur, Université de Bourgogne, BP 47870, 21078 Dijon, France;Laboratoire Le2i, UMR CNRS 5158 Aile des Sciences de l'ingénieur, Université de Bourgogne, BP 47870, 21078 Dijon, France
Venue:
Real-Time Imaging
Year:
2003

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Abstract

A real-time implementation of an approximation of the support vector machine (SVM) decision rule is proposed. This method is based on an improvement of a supervised classification method using hyperrectangles, which is useful for real-time image segmentation. The final decision combines the accuracy of the SVM learning algorithm and the speed of a hyperrectangles-based method. We review the principles of the classification methods and we evaluate the hardware implementation cost of each method. We present the combination algorithm, which consists of rejecting ambiguities in the learning set using SVM decision, before using the learning step of the hyperrectangles-based method. We present results obtained using Gaussian distribution and give an example of image segmentation from an industrial inspection problem. The results are evaluated regarding hardware cost as well as classification performances.