Evaluation of color spaces for robust image segmentation

In this paper, we evaluate the robustness of our color-based segmentation approach in combination with different color spaces, namely RGB, L*a*b*, HSV, and log-chromaticity (LCCS). For this purpose, we describe our deterministic segmentation algorithm including its gradually transformation of pixel-precise image data into a less error-prone and therefore more robust statistical representation in terms of moments. To investigate the robustness of a specific segmentation setting, we introduce our evaluation framework that directly works on the statistical representation. It is based on two different types of robustness measures, namely relative and absolute robustness. While relative robustness measures stability of segmentation results over time, absolute robustness measures stability regarding varying illumination by comparing results with ground truth data. The significance of these robustness measures is shown by evaluating our segmentation approach with different color spaces. For the evaluation process, an artificial scene was chosen as representative for application scenarios based on artificial landmarks.