Color descriptors for object category recognition

Category recognition is important to access visual information on the level of objects. A common approach is to compute image descriptors first and then to apply machine learning to achieve category recognition from annotated examples. As a consequence, the choice of image descriptors is of great influence on the recognition accuracy. So far, intensity-based (e.g. SIFT) descriptors computed at salient points have been used. However, color has been largely ignored. The question is, can color information improve accuracy of category recognition?
Therefore, in this paper, we will extend both salient point detection and region description with color information. The extension of color descriptors is integrated into the framework of category recognition enabling to select both intensity and color variants. Our experiments on an image benchmark show that category recognition benefits from the use of color. Moreover, the combination of intensity and color descriptors yields a 30% improvement over intensity features alone.