Classification boundary approximation by using combination of training steps for real-time image segmentation

Authors:
Johel Mitéran;Sebastien Bouillant;Elbey Bourennane
Affiliations:
Le2i, FRE, CNRS, Aile des Sciences de l'Ingénieur, Université de Bourgogne, Dijon, France;Le2i, FRE, CNRS, Aile des Sciences de l'Ingénieur, Université de Bourgogne, Dijon, France;Le2i, FRE, CNRS, Aile des Sciences de l'Ingénieur, Université de Bourgogne, Dijon, France
Venue:
MLDM'03 Proceedings of the 3rd international conference on Machine learning and data mining in pattern recognition
Year:
2003

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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.