Attributes Reduction Applied to Leather Defects Classification

  • Authors:

  • Willian Paraguassu Amorim;Hemerson Pistori;Mauro Conti Pereira;Manuel Antonio Chagas Jacinto

  • Affiliations:

  • -;-;-;-

  • Venue:
  • SIBGRAPI '10 Proceedings of the 2010 23rd SIBGRAPI Conference on Graphics, Patterns and Images
  • Year:
  • 2010

  • Porosity detection by using improved local binary patterns

    EHAC'12/ISPRA/NANOTECHNOLOGY'12 Proceedings of the 11th WSEAS international conference on Electronics, Hardware, Wireless and Optical Communications, and proceedings of the 11th WSEAS international conference on Signal Processing, Robotics and Automation, and proceedings of the 4th WSEAS international conference on Nanotechnology

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Abstract

This paper presents a study on attributes reduction, comparing five discriminant analysis techniques: FisherFace, CLDA, DLDA, YLDA and KLDA. Attributes reduction has been applied to the problem of leather defect classification using four different classifiers: C4.5, kNN, Na\"{i}ve Bayes and Support Vector Machines. The results of several experiments on the performance of discriminant analysis applied to the problem of defect detection are reported.