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Learning a gaussian basis for spectra representation aimed at reflectance classification
conference contribution
posted on 2011-01-01, 00:00 authored by Antonio Robles-KellyAntonio Robles-KellyIn this paper, we present a method which aims at learning a Gaussian basis which can be used to represent the reflectance spectra in the image while yielding a high recognition rate when used as input to an SVM classifier. To do this, we view the reflectance spectra as a Gaussian mixture and depart from a maximum-likelihood formulation which allows the introduction of posterior probabilities as a means to computing the mixture weights. This formulation permits the update of the Gaussian basis parameters, i.e. means and variances, through a two-step iterative optimisation process reminiscent of the EM algorithm. The first step of the algorithm estimates the posterior probabilities whereas the second step employs the dual formulation of the SVM classifier to update the Gaussian parameters. As a result, our method learns the Gaussian basis for the reflectance in the image subject to the performance of the SVM. We provide results on skin recognition and ground cover classification on remote sensing data. We also compare our results with those obtained using a number of alternatives. © 2011 IEEE.
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88 - 95Publisher DOI
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2160-7508eISSN
2160-7516ISBN-13
9781457705298Publication classification
E1.1 Full written paper - refereedTitle of proceedings
IEEE Computer Society Conference on Computer Vision and Pattern Recognition WorkshopsUsage metrics
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