DocumentCode :
2228984
Title :
Texture analysis for seabed classification: co-occurrence matrices vs. self-organizing maps
Author :
Pican, N. ; Trucco, E. ; Ross, M. ; Lane, D.M. ; Petillot, Y. ; Ruiz, I. Tena
Author_Institution :
Lab. of Ocean Syst., Heriot-Watt Univ., Edinburgh, UK
Volume :
1
fYear :
1998
fDate :
28 Sep-1 Oct 1998
Firstpage :
424
Abstract :
Considers two well-known pattern recognition techniques using texture analysis. The first is the co-occurrence matrix method which relies on statistics and the second is the Kohonen map which comes from the artificial neural networks domain. Both methods are used as feature extraction methods. The extracted feature vectors are fed to a second Kohonen map used as classifier. The authors report briefly some results of their experimental assessment of the merit of each technique when applied to the problem of classifying the seabed from sequences of real images
Keywords :
geophysical signal processing; image classification; image colour analysis; image texture; matrix algebra; oceanographic techniques; self-organising feature maps; video signal processing; Kohonen map; artificial neural networks; co-occurrence matrices; co-occurrence matrix method; experimental assessment; feature extraction methods; image sequences; pattern recognition techniques; seabed classification; self-organizing maps; texture analysis; Artificial neural networks; Data mining; Feature extraction; Image texture analysis; Laboratories; Oceans; Pattern analysis; Pattern recognition; Self organizing feature maps; Statistics;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
OCEANS '98 Conference Proceedings
Conference_Location :
Nice
Print_ISBN :
0-7803-5045-6
Type :
conf
DOI :
10.1109/OCEANS.1998.725781
Filename :
725781
Link To Document :
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