OSA's Digital Library

Applied Optics

Applied 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)

View Full Text Article

Enhanced HTML    Acrobat PDF (1511 KB)

Browse Journals / Lookup Meetings

Browse by Journal and Year


Lookup Conference Papers

Close Browse Journals / Lookup Meetings

Article Tools



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

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

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)

Sort:  Year  |  Journal  |  Reset  


  1. K. Song, J. Kittler, and M. Petrou, "Defect detection in random color textures," Image Vis. Comput. 14, 667-683 (1996). [CrossRef]
  2. F. Lumbreras, R. Baldrich, M. Vanrell, J. Serrat, and J. J. Villanueva, "Multiresolution color texture classification of ceramic tiles," in Recent Research Developments in Optical Engineering (Research Signpost, 1999), Vol. 2, pp. 213-228.
  3. M. L. Smith and R. J. Stamp, "Automated inspection of textured ceramic tiles," Comput. Ind. 43, 73-82 (2000). [CrossRef]
  4. G. Paschos, "Fast color texture recognition using chromaticity moments," Pattern Recogn. Lett. 21, 837-841 (2000). [CrossRef]
  5. S. Kukkonen, H. Kälviäinen, and J. Parkkinen, "Color features for quality control in ceramic tile industry," Opt. Eng. 40, 170-177 (2001). [CrossRef]
  6. R. O. Duda, P. E. Hart, and D. G. Stork, "Pattern Classification," 2nd ed. (Wiley, 2001).
  7. M. Unser, "A fast texturé classifier based on cross entropy minimization," in Proceedings of the EUSIPCO'83 (North-Holland, 1983), pp. 261-264.
  8. K. Fukunaga, Introduction to Statistical Pattern Recognition, 2nd ed. (Academic, 1990).
  9. K. Irahama and Y. Furukawa, "Gradient descent learning of nearest neighbor classifiers with outlier rejection," Pattern Recogn. 28, 761-768 (1995). [CrossRef]
  10. M. M. Moya and D. R. Hush, "Network constraints and multi-objective optimization for one-class classification," Neural Networks 9, 463-474 (1996). [CrossRef]
  11. D. M. J. Tax and R. P. W. Duin, "Support vector domain description," Pattern Recogn. Lett. 20, 1191-1199 (1999). [CrossRef]
  12. D. M. J. Tax, "One-class classification: concept-learning in the absence of counterexamples," Ph.D. dissertation (Delft University of Technology, 2001).
  13. D. M. J. Tax and R. P. W. Duin, "Uniform object generation for optimizing one-class classifiers," J. Mach. Learn. Res. 2, 155-173 (2002).
  14. P. Juszczak, "Learning to recognize: a study on one-class classification and active learning," Ph.D. dissertation (Delft University of Technology, 2006).
  15. R. E. Sánchez-Yáñez, E. V. Kurmyshev, and F. J. Cuevas, "A framework for texture classification using the coordinated clusters representation," Pattern Recogn. Lett. 24, 21-31 (2003). [CrossRef]
  16. R. E. Sánchez-Yáñez, E. V. Kurmyshev, and A. Fernández, "One-class texture classifier in the CCR feature space," Pattern Recogn. Lett. 24, 1503-1511 (2003). [CrossRef]
  17. E. V. Kurmyshȩv and R. E. Sánchez-Yáñez, "Comparative experiment with colour texture classifier using the CCR feature space," Pattern Recogn. Lett. 26, 1346-1353 (2005). [CrossRef]
  18. E. V. Kurmyshev, M. Poterasu, and J. T. Guillen-Bonilla, "Image scale determination for optimal texture classification using coordinated clusters representation," Appl. Opt. 46, 1467-1476 (2007). [CrossRef] [PubMed]
  19. J. T. Guillen-Bonilla, E. Kurmyshev, and A. Fernández, "Quantifying a similarity of classes of texture images," Appl. Opt. 46, 5562-5570 (2007). [CrossRef] [PubMed]
  20. E. V. Kurmyshev, "Classification of texture images using coordinated clusters representation, in Recent Advances in Optical Metrology (Research Signpost, 2007), pp. 155-226.
  21. A. M. Molinaro, R. Simon, and R. M. Pfeiffer, "Prediction error estimation: a comparison of resampling methods," Bioinformatics 21, 3301-3307 (2005). [CrossRef] [PubMed]
  22. OuTex, Texture Database, http://www.outex.oulu.fi/temp/.

Cited By

Alert me when this paper is cited

OSA is able to provide readers links to articles that cite this paper by participating in CrossRef's Cited-By Linking service. CrossRef includes content from more than 3000 publishers and societies. In addition to listing OSA journal articles that cite this paper, citing articles from other participating publishers will also be listed.


Fig. 1 Fig. 2

« Previous Article  |  Next Article »

OSA is a member of CrossRef.

CrossCheck Deposited