1 - Spectral Analysis and Unsupervised SVM Classification for Skin Hyper-pigmentation Classification.
S. Prigent et X. Descombes et D. Zugaj et J. Zerubia. Dans Proc. IEEE Workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS), Reykjavik, Iceland, juin 2010.
Mots-clés : Sectral analysis, Data reduction, Projection pursuit, Support Vector Machines, skin hyper-pigmentation.
@INPROCEEDINGS{sp01,
|
| author |
= |
{Prigent, S. and Descombes, X. and Zugaj, D. and Zerubia, J.}, |
| title |
= |
{Spectral Analysis and Unsupervised SVM Classification for Skin Hyper-pigmentation Classification}, |
| year |
= |
{2010}, |
| month |
= |
{juin}, |
| booktitle |
= |
{Proc. IEEE Workshop on Hyperspectral Image and Signal Processing : Evolution in Remote Sensing (WHISPERS)}, |
| address |
= |
{Reykjavik, Iceland}, |
| pdf |
= |
{http://hal.inria.fr/docs/00/49/55/60/PDF/whispers2010_submission_124.pdf}, |
| keyword |
= |
{Sectral analysis, Data reduction, Projection pursuit, Support Vector Machines, skin hyper-pigmentation} |
| } |
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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