Texture analysis for seabed classification: co-occurrence matrices vs. self-organizing maps

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.

[1]  Brian V. Funt,et al.  Color Constancy for Scenes with Varying Illumination , 1997, Comput. Vis. Image Underst..

[2]  J. Kittler,et al.  Colour texture analysis using colour histogram , 1994 .

[3]  Robert M. Haralick,et al.  Textural Features for Image Classification , 1973, IEEE Trans. Syst. Man Cybern..

[4]  J. M. Hans du Buf,et al.  A review of recent texture segmentation and feature extraction techniques , 1993 .

[5]  Anil K. Jain,et al.  Unsupervised texture segmentation using Gabor filters , 1990, 1990 IEEE International Conference on Systems, Man, and Cybernetics Conference Proceedings.

[6]  Bt Thomas,et al.  Segmentation of natural images using self-organising feature maps , 1996 .

[7]  Calvin C. Gotlieb,et al.  Texture descriptors based on co-occurrence matrices , 1990, Comput. Vis. Graph. Image Process..

[8]  Teuvo Kohonen,et al.  Self-Organizing Maps , 2010 .