Color classification using adaptive dichromatic model

Color-based vision applications face the challenge that colors are variant to illumination. In this paper we present a color classification algorithm that is adaptive to continuous variable lighting. Motivated by the dichromatic color reflectance model, we use a Gaussian mixture model (GMM) of two components to model the distribution of a color class in the YUV color space. The GMM is derived from the classified color pixels using the standard expectation-maximization (EM) algorithm, and the color model is iteratively updated over time. The novel contribution of this work is the theoretical analysis supported by experiments - that a GMM of two components is an accurate and complete representation of the color distribution of a dichromatic surface