DocumentCode
327829
Title
MDL-based design of vector quantizers
Author
Bischof, Horst ; Leonardis, Ales
Author_Institution
Pattern Recognition & Image Process. Group, Wien Univ., Austria
Volume
1
fYear
1998
fDate
16-20 Aug 1998
Firstpage
891
Abstract
We develop a framework for vector quantization networks based on the minimum description length (MDL) principle. This MDL framework is used to derive conditions for the removal of superfluous units from the network. We design a computationally efficient algorithm for finding the optimal number of reference vectors as well as their positions. We illustrate our approach on 2D clustering problems and present applications on image coding
Keywords
image coding; minimisation; neural nets; probability; unsupervised learning; vector quantisation; 2D clustering; image coding; minimisation; minimum description length; neural nets; probability distribution; superfluous unit removal; unsupervised learning; vector quantization; Clustering algorithms; Distortion measurement; Encoding; Image coding; Neural networks; Pattern recognition; Read only memory; Stochastic processes; Unsupervised learning; Vector quantization;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 1998. Proceedings. Fourteenth International Conference on
Conference_Location
Brisbane, Qld.
ISSN
1051-4651
Print_ISBN
0-8186-8512-3
Type
conf
DOI
10.1109/ICPR.1998.711293
Filename
711293
Link To Document