Generalized co-occurrence matrix for multispectral texture analysis

We present a new co-occurrence matrix based approach for multispectral texture analysis. The spectral and spatial domains of the multispectral textures are processed separately. The color space used in this study is represented by subspaces and it is classified by the averaged learning subspace meth...

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Bibliographic Details
Published inProceedings of 13th International Conference on Pattern Recognition Vol. 2; pp. 785 - 789 vol.2
Main Authors Hauta-Kasari, M., Parkkinen, J., Jaaskelainen, T., Lenz, R.
Format Conference Proceeding
LanguageEnglish
Published IEEE 1996
Subjects
Image analysis
Image color analysis
Image processing
Image segmentation
Image texture analysis
Information technology
Laboratories
Multispectral imaging
Optical imaging
Spectroscopy
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ISBN9780818672828
081867282X
ISSN1051-4651
DOI10.1109/ICPR.1996.546930

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Summary:We present a new co-occurrence matrix based approach for multispectral texture analysis. The spectral and spatial domains of the multispectral textures are processed separately. The color space used in this study is represented by subspaces and it is classified by the averaged learning subspace method (ALSM). In the spatial domain we use a generalized co-occurrence matrix for vector valued pixels. The texture feature vectors are classified by the k-nearest neighbor (KNN) classifier and the multilayer perceptron (MLP) network. Experimental results of the multispectral texture segmentation are presented.
ISBN:9780818672828
081867282X
ISSN:1051-4651
DOI:10.1109/ICPR.1996.546930