Fraunhofer-Gesellschaft

Publica

Hier finden Sie wissenschaftliche Publikationen aus den Fraunhofer-Instituten.

Adapting information theoretic clustering to binary images

 
: Bauckhage, C.; Thurau, C.

:
Volltext urn:nbn:de:0011-n-1434991 (694 KByte PDF)
MD5 Fingerprint: 45bf799fafe1907da3e6539de5556665
© 2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE.
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
S.910-913
International Conference on Pattern Recognition (ICPR) <20, 2010, Istanbul>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IAIS ()

Abstract
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.

: http://publica.fraunhofer.de/dokumente/N-143499.html