Learning Invariants to Illumination Changes Typical of Indoor Environments: Application to Image Color Correction

摘要

This paper presents a new approach for automatic image color correction, based on statistical learning: The method both parameterizes color independently of illumination and corrects color for changes of illumination. This is useful in many image processing applications, such as image segmentation or background subtraction. The motivation for using a learning approach is to deal with changes of lighting typical of indoor environments such as home and office. The method is based on learning color invariants using a modified multi-layer perceptron (MLP). The MLP is odd-layered. The middle layer includes two neurons which estimate two color invariants and one input neuron which takes in the luminance desired in output of the MLP. The advantage of the modified MLP over a classical MLP is better performance and the estimation of invariants to illumination. The trained modified MLP can be applied using look-up tables, yielding very fast processing. Results illustrate the approach and compare it with other color correction approaches from the literature.