DocumentCode
324512
Title
Application of neural “gas” model in image compression
Author
Zhang, Bai-ling ; Fu, Min-yue ; Yan, Hong
Author_Institution
Dept. of Electr. & Comput. Eng., Newcastle Univ., NSW, Australia
Volume
2
fYear
1998
fDate
4-9 May 1998
Firstpage
918
Abstract
We propose to apply a topology representing learning algorithm, the neural “gas” model, for obtaining topology ordered codebook for the vector quantization and exploit it on image compression. Compared with the well-known Kohonen´s self-organizing map, the neural “gas” model has several advantages, including faster convergence and higher signal-to-noise ratio in reconstruction. We illustrate some experimental results and discuss several relevant research issues
Keywords
convergence; image processing; learning (artificial intelligence); network topology; neural nets; vector quantisation; codebook; convergence; image compression; learning algorithm; neural gas model; topology; vector quantization; Application software; Australia; Clustering algorithms; Convergence; Image coding; Image reconstruction; Image storage; Neurons; Topology; Vector quantization;
fLanguage
English
Publisher
ieee
Conference_Titel
Neural Networks Proceedings, 1998. IEEE World Congress on Computational Intelligence. The 1998 IEEE International Joint Conference on
Conference_Location
Anchorage, AK
ISSN
1098-7576
Print_ISBN
0-7803-4859-1
Type
conf
DOI
10.1109/IJCNN.1998.685891
Filename
685891
Link To Document