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Adapting information theoretic clustering to binary images

: Bauckhage, C.; Thurau, C.

Volltext urn:nbn:de:0011-n-1434991 (694 KByte PDF)
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Erstellt am: 30.10.2010

International Association for Pattern Recognition -IAPR-; Institute of Electrical and Electronics Engineers -IEEE-:
ICPR 2010, 20th International Conference on Pattern Recognition. Proceedings : 23-26 August, 2010, Istanbul, Turkey
Piscataway, NJ: IEEE, 2010
ISBN: 978-0-7695-4109-9
ISBN: 978-1-4244-7542-1
ISBN: 1-4244-7542-2
International Conference on Pattern Recognition (ICPR) <20, 2010, Istanbul>
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IAIS ()

We consider the problem of finding points of interest along local curves of binary images. Information theoretic vector quantization is a clustering algorithm that shifts cluster centers towards the modes of principal curves of a data set. Its runtime characteristics, however, do not allow for efficient processing of many data points. In this paper, we show how to solve this problem when dealing with data on a 2D lattice. Borrowing concepts from signal processing, we adapt information theoretic clustering to the quantization of binary images and gain significant speedup.