Learning a Gaussian basis for spectra representation aimed at reflectance classification
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Description
In 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...[Show more]
Collections | ANU Research Publications |
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Date published: | 2011 |
Type: | Conference paper |
URI: | http://hdl.handle.net/1885/63695 |
Source: | Graph connectivity in sparse subspace clustering |
DOI: | 10.1109/CVPRW.2011.5981791 |
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File | Description | Size | Format | Image |
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01_Robles-Kelly_Learning_a_Gaussian_basis_for_2011.pdf | 2.08 MB | Adobe PDF | Request a copy |
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