DocumentCode
2492907
Title
Hierarchies of autoassociators
Author
Weingessel, A. ; Bischof, H. ; Hornik, K.
Author_Institution
Inst. fur Statistik und Wahrscheinlichkeitstheorie, Tech. Univ. Wien, Austria
Volume
4
fYear
1996
fDate
25-29 Aug 1996
Firstpage
200
Abstract
The principal component pyramid is a hierarchical neural network which can successfully be employed in image compression and feature extraction of images. Previously, the construction of the network from the corresponding pyramid was done on a case by case basis. In this paper we automate this process by giving formulas describing the size of the network and the number of weight constraints in the net
Keywords
associative processing; data compression; feature extraction; image processing; network topology; neural nets; autoassociators; feature extraction; hierarchical neural network; image compression; network size; network topology; principal component pyramid; weight constraints; Feature extraction; Filtering; Image analysis; Image coding; Image sampling; Network topology; Neural networks; Principal component analysis;
fLanguage
English
Publisher
ieee
Conference_Titel
Pattern Recognition, 1996., Proceedings of the 13th International Conference on
Conference_Location
Vienna
ISSN
1051-4651
Print_ISBN
0-8186-7282-X
Type
conf
DOI
10.1109/ICPR.1996.547415
Filename
547415
Link To Document