A spectral reflectance representation for recognition and reproduction

In this paper we present a method to recover a spectra representation for reproduction and recognition on multispectral imagery. To do this, we commence by viewing the spectra in the image as a mixture which can be expressed in terms of the sample mean and a set of basis vectors and weights. This treatment leads to an MAP approach where the sample means is given by the centers yielded by the application of the k-means clustering algorithm and the basis vectors are the eigenvectors of the corresponding covariance matrix. We compute the weights making use of a linear programming approach. We illustrate the utility of the method for purposes of skin recognition and spectra reconsruction.