Partitioned iterated function systems with division and a fractal dependence graph in recognition of 2D shapes

Krzysztof Gdawiec; Diana Domańska

International Journal of Applied Mathematics and Computer Science (2011)

  • Volume: 21, Issue: 4, page 757-767
  • ISSN: 1641-876X

Abstract

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One of the approaches in pattern recognition is the use of fractal geometry. The property of self-similarity of fractals has been used as a feature in several pattern recognition methods. All fractal recognition methods use global analysis of the shape. In this paper we present some drawbacks of these methods and propose fractal local analysis using partitioned iterated function systems with division. Moreover, we introduce a new fractal recognition method based on a dependence graph obtained from the partitioned iterated function system. The proposed method uses local analysis of the shape, which improves the recognition rate. The effectiveness of our method is shown on two test databases. The first one was created by the authors and the second one is the MPEG7 CE-Shape-1PartB database. The obtained results show that the proposed methodology has led to a significant improvement in the recognition rate.

How to cite

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Krzysztof Gdawiec, and Diana Domańska. "Partitioned iterated function systems with division and a fractal dependence graph in recognition of 2D shapes." International Journal of Applied Mathematics and Computer Science 21.4 (2011): 757-767. <http://eudml.org/doc/208086>.

@article{KrzysztofGdawiec2011,
abstract = {One of the approaches in pattern recognition is the use of fractal geometry. The property of self-similarity of fractals has been used as a feature in several pattern recognition methods. All fractal recognition methods use global analysis of the shape. In this paper we present some drawbacks of these methods and propose fractal local analysis using partitioned iterated function systems with division. Moreover, we introduce a new fractal recognition method based on a dependence graph obtained from the partitioned iterated function system. The proposed method uses local analysis of the shape, which improves the recognition rate. The effectiveness of our method is shown on two test databases. The first one was created by the authors and the second one is the MPEG7 CE-Shape-1PartB database. The obtained results show that the proposed methodology has led to a significant improvement in the recognition rate.},
author = {Krzysztof Gdawiec, Diana Domańska},
journal = {International Journal of Applied Mathematics and Computer Science},
keywords = {fractal; partitioned iterated function system; shape recognition; dependence graph},
language = {eng},
number = {4},
pages = {757-767},
title = {Partitioned iterated function systems with division and a fractal dependence graph in recognition of 2D shapes},
url = {http://eudml.org/doc/208086},
volume = {21},
year = {2011},
}

TY - JOUR
AU - Krzysztof Gdawiec
AU - Diana Domańska
TI - Partitioned iterated function systems with division and a fractal dependence graph in recognition of 2D shapes
JO - International Journal of Applied Mathematics and Computer Science
PY - 2011
VL - 21
IS - 4
SP - 757
EP - 767
AB - One of the approaches in pattern recognition is the use of fractal geometry. The property of self-similarity of fractals has been used as a feature in several pattern recognition methods. All fractal recognition methods use global analysis of the shape. In this paper we present some drawbacks of these methods and propose fractal local analysis using partitioned iterated function systems with division. Moreover, we introduce a new fractal recognition method based on a dependence graph obtained from the partitioned iterated function system. The proposed method uses local analysis of the shape, which improves the recognition rate. The effectiveness of our method is shown on two test databases. The first one was created by the authors and the second one is the MPEG7 CE-Shape-1PartB database. The obtained results show that the proposed methodology has led to a significant improvement in the recognition rate.
LA - eng
KW - fractal; partitioned iterated function system; shape recognition; dependence graph
UR - http://eudml.org/doc/208086
ER -

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