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Applied Optics

Applied Optics

APPLICATIONS-CENTERED RESEARCH IN OPTICS

  • Editor: James C. Wyant
  • Vol. 47, Iss. 4 — Feb. 1, 2008
  • pp: 541–547

Algorithm for training the minimum error one-class classifier of images

J. T. Guillen-Bonilla, E. Kurmyshev, and E. González  »View Author Affiliations


Applied Optics, Vol. 47, Issue 4, pp. 541-547 (2008)
http://dx.doi.org/10.1364/AO.47.000541


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Abstract

We propose a training algorithm for one-class classifiers in order to minimize the classification error. The aim is to choose the optimal value of the slack parameter, which controls the selectiveness of a classifier. The one-class classifier based on the coordinated clusters representation of images is trained and then used for the classification of texture images. As the slack parameter C varies through a range of values, for each C, the misclassification rate is computed using only the training samples. The value of C that yields the minimum misclassification rate, estimated over the training set, is taken as the optimal value, C opt . Finally, the optimized classifier is tested on the extended database of images. Experimental results demonstrate the validity of the proposed method. In our experiments, classification efficiency approaches, or is equal to, 100%, after the optimal training of the classifier.

© 2008 Optical Society of America

OCIS Codes
(100.5010) Image processing : Pattern recognition
(150.3040) Machine vision : Industrial inspection
(100.3008) Image processing : Image recognition, algorithms and filters

ToC Category:
Image Processing

History
Original Manuscript: October 1, 2007
Revised Manuscript: December 10, 2007
Manuscript Accepted: December 12, 2007
Published: January 25, 2008

Citation
J. T. Guillen-Bonilla, E. Kurmyshev, and E. González, "Algorithm for training the minimum error one-class classifier of images," Appl. Opt. 47, 541-547 (2008)
http://www.opticsinfobase.org/ao/abstract.cfm?URI=ao-47-4-541

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