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Learning a Color Algorithm from Examples

Author(s)
Hurlbert, Anya; Poggio, Tomaso
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Abstract
We show that a color algorithm capable of separating illumination from reflectance in a Mondrian world can be learned from a set of examples. The learned algorithm is equivalent to filtering the image data---in which reflectance and illumination are mixed---through a center-surround receptive field in individual chromatic channels. The operation resembles the "retinex" algorithm recently proposed by Edwin Land. This result is a specific instance of our earlier results that a standard regularization algorithm can be learned from examples. It illustrates that the natural constraints needed to solve a problemsin inverse optics can be extracted directly from a sufficient set of input data and the corresponding solutions. The learning procedure has been implemented as a parallel algorithm on the Connection Machine System.
Date issued
1987-06-01
URI
http://hdl.handle.net/1721.1/5601
Other identifiers
AIM-909
Series/Report no.
AIM-909
Keywords
computer vision, color constancy, learning, regularization, soptimal estimation, pseudoinverse

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