DocumentCode :
924901
Title :
Strategies for unsupervised multimedia processing: self-organizing trees and forests
Author :
Kyan, Matthew ; Jarrah, Kambiz ; Muneesawang, Paisarn ; Guan, Ling
Author_Institution :
Ryerson Univ., Toronto, Ont.
Volume :
1
Issue :
2
fYear :
2006
fDate :
5/1/2006 12:00:00 AM
Firstpage :
27
Lastpage :
40
Abstract :
In this article, we explore a new family of neural network architectures that have a basis in self-organization, yet are somewhat free from many of the constraints typical of other well-known self-organizing architectures. Within this family, the basic processing unit is known as the self-organizing tree map (SOTM). We will look at how this model has evolved since its inception in 1995, how it has inspired new models, and how it is being applied to complex multimedia research problems in digital asset management and microbiological image analysis
Keywords :
multimedia systems; neural net architecture; self-organising feature maps; trees (mathematics); digital asset management; microbiological image analysis; neural network architectures; self-organizing forests; self-organizing tree map; unsupervised multimedia processing; Automation; Competitive intelligence; Computational intelligence; Computer architecture; Computer networks; Data mining; Humans; Image analysis; Information analysis; Unsupervised learning;
fLanguage :
English
Journal_Title :
Computational Intelligence Magazine, IEEE
Publisher :
ieee
ISSN :
1556-603X
Type :
jour
DOI :
10.1109/MCI.2006.1626492
Filename :
1626492
Link To Document :
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