Texture Learning by Fractal Compression

Benoît Dolez; Nicole Vincent

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Paper

Texture Learning by Fractal Compression

In Proceedings of the 7th International Workshop on Pattern Recognition in Information Systems - Volume 0ICEIS, 99-106, 2007 , Funchal, Madeira, Portugal

Authors: Benoît Dolez ¹ and Nicole Vincent ²

Affiliations: ¹ CRIP5-SIP, René Descartes University; SAGEM DS, France ; ² CRIP5-SIP, René Descartes University, France

Abstract: This paper proposes a texture learning method based on fractal compression. This type of compression is efficient for extracting self-similarities in an image. Square blocks are studied and similarities between them highlighted. This allows establishing a score and thus a rating of the blocks. Selecting the blocks that best encode the biggest part of the image or a texture leads to a database of the most representative ones. The recognition step consists in labeling blocks and pixels of a new image. The blocks of the new image are matched with the ones of the different texture databases. As an application, we used our method to recognize vegetation and buildings on aerial images.

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Paper citation in several formats:

Dolez, B. and Vincent, N. (2007). Texture Learning by Fractal Compression. In Proceedings of the 7th International Workshop on Pattern Recognition in Information Systems (ICEIS 2007) - PRIS; ISBN 978-972-8865-93-1, SciTePress, pages 99-106. DOI: 10.5220/0002429200990106

@conference{pris07,
author={Benoît Dolez. and Nicole Vincent.},
title={Texture Learning by Fractal Compression},
booktitle={Proceedings of the 7th International Workshop on Pattern Recognition in Information Systems (ICEIS 2007) - PRIS},
year={2007},
pages={99-106},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0002429200990106},
isbn={978-972-8865-93-1},
}

TY - CONF

JO - Proceedings of the 7th International Workshop on Pattern Recognition in Information Systems (ICEIS 2007) - PRIS
TI - Texture Learning by Fractal Compression
SN - 978-972-8865-93-1
AU - Dolez, B.
AU - Vincent, N.
PY - 2007
SP - 99
EP - 106
DO - 10.5220/0002429200990106
PB - SciTePress