Efficient Color Representation for Image Segmentation under Non-white Illumination

Color image segmentation algorithms often consider object color to be a constant property of an object. If the light source dominantly exhibits a particular color, however, it becomes necessary to consider the color variation induced by the colored illuminant. This paper presents a new approach to segmenting color images that are photographed under non-white illumination conditions. It also addresses how to estimate the color of illuminant in terms of the standard RGB color values rather than the spectrum of the illuminant. With respect to the illumination axis that goes through the origin and the centroid of illuminant color clusters (prior given by the estimation process), the RGB color space is transformed into our new color coordinate system. Our new color scheme shares the intuitiveness of the HSI (HSL or HSV) space that comes from the conical (double-conical or cylindrical) structure of hue and saturation aligned with the intensity variation at its center. It has been developed by locating the ordinary RGB cube in such a way that the illumination axis aligns with the vertical axis (Z-axis) of a larger Cartesian (XYZ) space. The work in this paper uses the dichromatic reflection model [1] to interpret the physics about light and optical effects in color images. The linearity proposed in the dichromatic reflection model is essential and is well preserved in the RGB color space. By proposing a straightforward color model transduction, we suggest dimensionality reduction and provide an efficient way to analyze color images of dielectric objects under non-white illumination conditions. The feasibility of the proposed color representation has been demonstrated by our experiment that is twofold: 1) Segmentation result from a multi-modal histogram-based thresholding technique and 2) Color constancy result from discounting illumination effect further by color balancing.