Color Object Recognition in Real-World Scenes

This work investigates the role of color in object recognition. We approach the problem from a computational perspective by measuring the performance of biologically inspired object recognition methods. As benchmarks, we use image datasets proceeding from a real-world object detection scenario and compare classification performance using color and gray-scale versions of the same datasets. In order to make our results as general as possible, we consider object classes with and without intrinsic color, partitioned into 4 datasets of increasing difficulty and complexity. For the same reason, we use two independent bio-inspired models of object classification which make use of color in different ways. We measure the qualitative dependency of classification performance on classifier type and dataset difficulty (and used color space) and compare to results on gray-scale images. Thus, we are able to draw conclusions about the role and the optimal use of color in classification and find that our results are in good agreement with recent psychophysical results.