Manifold of color perception: color constancy using a nonlinear line of attraction

Ming-Jung Seow; Vijayan K. Asari

doi:10.1117/12.643158

17 February 2006 Manifold of color perception: color constancy using a nonlinear line of attraction

Ming-Jung Seow, Vijayan K. Asari

Proceedings Volume 6064, Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning; 60641F (2006) https://doi.org/10.1117/12.643158
Event: Electronic Imaging 2006, 2006, San Jose, California, United States

Abstract

In this paper, we propose the concept of manifold of color perception based on an observation that the perceived color in a set of similar color images defines a manifold in the high dimensional space. Such a manifold representation can be learned from a few images of similar color characteristics. This learned manifold can then be used as a basis for color correction of the images having different color perception to the previously learned color. To learn the manifold for color perception, we propose a novel learning algorithm based on a recurrent neural network. Unlike the conventional recurrent neural network model in which the memory is stored in an attractive fixed point at discrete locations in the state space, the dynamics of the proposed learning algorithm represents memory as a line of attraction. The region of convergence at the line of attraction is defined by the statistical characteristics of the training data. We demonstrate experimentally how we can use the proposed manifold to color-balance the common lighting variations in the environment.

Citation Download Citation

Ming-Jung Seow and Vijayan K. Asari "Manifold of color perception: color constancy using a nonlinear line of attraction", Proc. SPIE 6064, Image Processing: Algorithms and Systems, Neural Networks, and Machine Learning, 60641F (17 February 2006); https://doi.org/10.1117/12.643158

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