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