Learning a Color Algorithm from Examples

A lightness algorithm that separates surface reflectance from illumination in a Mondrian world is synthesized automatically from a set of examples, pairs of input (image irradiance) and desired output (surface reflectance). The algorithm, which re(cid:173) sembles a new lightness algorithm recently proposed by Land, is approximately equiva(cid:173) lent to filtering the image through a center-surround receptive field in individual chro(cid:173) matic channels. The synthesizing technique, optimal linear estimation, requires only one assumption, that the operator that transforms input into output is linear. This assumption is true for a certain class of early vision algorithms that may therefore be synthesized in a similar way from examples. Other methods of synthesizing algorithms from examples, or "learning", such as backpropagation, do not yield a significantly dif(cid:173) ferent or better lightness algorithm in the Mondrian world.