Pattern Classification with Rejection Using Cellular Automata-Based Filtering

Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 10244)

Abstract

In this article we address the problem of contaminated data in pattern recognition tasks, where apart from native patterns we may have foreign ones that do not belong to any native class. We present a novel approach to image classification with foreign pattern rejection based on cellular automata. The method is based only on native patterns, so no knowledge about characteristics of foreign patterns is required at the stage of model construction. The proposed approach is evaluated in a study of handwritten digits recognition. As foreign patterns we use distorted digits. Experiments show that the proposed model classifies native patterns with a high success rate and rejects foreign patterns as well.

Keywords

Cellular Automaton Cellular Automaton Black Pixel Native Class Handwritten Digit 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

Notes

Acknowledgements

The research is supported by the National Science Center, grant no. 2012/07/B/ST6/01501, decision no. DEC-2012/07/B/ST6/01501.

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Copyright information

© IFIP International Federation for Information Processing 2017

Authors and Affiliations

  1. 1.Faculty of Mathematics and Information ScienceWarsaw University of TechnologyWarsawPoland

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