Indexing colored surfaces in images

We present a method of indexing color images based on color surface reflectance classes. Specifically, we analyse the distributions from members of a surface color class in a chrominance-based color space and show that they form collinear clusters under identical illumination. We also design an optimal color space based on the Fisher discriminant criterion to maximally separate members of different color surface classes. Finally, color indexing is achieved by finding regions in unsegmented images that belong to a queried color surface class using the location and orientation information of the color distributions in the optimal color space. Results are shown that indicate that using both location and orientation information of surface color in the optimal color space lowers the number of false positives and negatives in indexing