Feedforward neural networks for automated classification

Ivan Jordanov*, Antoniya Georgieva

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

We investigate an intelligent computer vision system that incorporates feedforward neural networks (NN) for recognition and classification of commercially available cork tiles. The system is capable of acquiring and processing gray images using several feature generation and analysis techniques. Its functionality includes image acquisition, feature extraction and preprocessing, and feature classification with NN. We also discuss system test and validation results from the recognition and classification tasks. The system investigation also includes statistical feature processing (features number and dimensionality reduction techniques) and classifier design and training. The NN are trained with our genetic low-discrepancy search method for global optimization (GLPτS), and demonstrate very good generalisation abilities when tested on unseen samples. In our view, the reported success rate of up to 95% is due to several factors: combination of feature generation techniques; application of Analysis of Variance (ANOVA) and Principal Component Analysis (PCA), which appeared to be very efficient for preprocessing the data; and also the use of suitable NN design and learning method.

Original languageEnglish
Title of host publicationProceedings of the 9th IEEE International Conference on Cognitive Informatics, ICCI 2010
PublisherIEEE
Pages578-582
Number of pages5
ISBN (Electronic)9781424480425
ISBN (Print)9781424480418
DOIs
Publication statusPublished - 11 Oct 2010
Event9th IEEE International Conference on Cognitive Informatics, ICCI 2010 - Beijing, China
Duration: 7 Jul 20109 Jul 2010

Conference

Conference9th IEEE International Conference on Cognitive Informatics, ICCI 2010
Country/TerritoryChina
CityBeijing
Period7/07/109/07/10

Keywords

  • ANOVA
  • Feedforward neural networks
  • Image recognition and processing
  • Machine vision
  • PCA

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