Multicomponent image segmentation: a comparative analysis between a hybrid genetic algorithm and self‐organizing maps

2009 ◽  
Vol 30 (3) ◽  
pp. 595-610 ◽  
Author(s):  
M. M. Awad ◽  
K. Chehdi ◽  
A. Nasri
Keyword(s):  
Genetic Algorithm ◽  
Image Segmentation ◽  
Comparative Analysis ◽  
Hybrid Genetic Algorithm ◽  
Self Organizing Maps ◽  
Self Organizing

Fast CEUS image segmentation based on self organizing maps

2014 ◽  
Author(s):  
Julie Paire ◽  
Vincent Sauvage ◽  
Adelaïde Albouy-Kissi ◽  
Viviane Ladam Marcus ◽  
Claude Marcus ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Self Organizing Maps ◽  
Self Organizing

118 Genetic Algorithm Using self-Organizing Maps

2008 ◽  
Vol 2008.8 (0) ◽  
pp. 93-96
Author(s):  
Shen Kan ◽  
Zhai Fei ◽  
Eisuke Kita
Keyword(s):  
Genetic Algorithm ◽  
Self Organizing Maps ◽  
Self Organizing

Color Image Segmentation of Seed Images Based on Self-Organizing Maps (SOM) Neural Network

2019 ◽  
Vol 23 (1) ◽  
Author(s):  
J. M. Barrón Adame ◽  
M. S. Acosta Navarrete ◽  
J. Quintanilla Domínguez ◽  
R. Guzmán Cabrera ◽  
M. Cano Contreras ◽  
...  
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Color Image ◽  
Color Image Segmentation ◽  
Self Organizing Maps ◽  
Som Neural Network ◽  
Self Organizing

Competition among European Tourist Cities: A Comparative Analysis with Multidimensional Scaling and Self-Organizing Maps

1995 ◽  
Vol 1 (3) ◽  
pp. 283-302 ◽  
Author(s):  
Josef A. Mazanec
Keyword(s):  
Comparative Analysis ◽  
Multidimensional Scaling ◽  
Profile Data ◽  
Self Organizing Maps ◽  
Tourist Destinations ◽  
European Cities ◽  
Som Network ◽  
Self Organizing

The tourism researcher's attention is drawn to the neurocomputing methodology of self-organizing maps (SOMs) SOMs can be used to find a radically parsimonious representation of multidimensional profile data. The properties of an SOM network are explored in a case study on the competitive relationships between European cities as tourist destinations. SOM results are compared with a conventional INDSCAL model. The database consists of the guest mix profiles of 26 major tourist cities between 1975 and 1992.

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