SPECTRAL ANALYSIS AND UNSUPERVISED SVM CLASSIFICATION FOR SKIN HYPER-PIGMENTATION CLASSIFICATION

Sylvain Prigent 1 Xavier Descombes 1 Didier Zugaj 2 Josiane Zerubia 1
1 ARIANA - Inverse problems in earth monitoring
CRISAM - Inria Sophia Antipolis - Méditerranée , SIS - Signal, Images et Systèmes
2 GALDERMA R&D
Abstract : Data reduction procedures and classification via support vector machines (SVMs) are often associated with multi or hyperspectral image analysis. In this paper, we propose an automatic method with these two schemes in order to perform a classification of skin hyper-pigmentation on multi-spectral images. We propose a spectral analysis method to partition the spectrum as a tool for data reduction, implemented by projection pursuit. Once the data is reduced, an SVM is used to differentiate the pathological from the healthy areas. As SVM is a supervised classification method, we propose a spatial criterion for spectral analysis in order to perform automatic learning.
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Communication dans un congrès
IEEE Workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (Whispers), Jun 2010, Reykjavik, Iceland. 2010
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Contributeur : Sylvain Prigent <>
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Dernière modification le : mercredi 14 décembre 2016 - 01:06:42
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Sylvain Prigent, Xavier Descombes, Didier Zugaj, Josiane Zerubia. SPECTRAL ANALYSIS AND UNSUPERVISED SVM CLASSIFICATION FOR SKIN HYPER-PIGMENTATION CLASSIFICATION. IEEE Workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (Whispers), Jun 2010, Reykjavik, Iceland. 2010. <inria-00495560>

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