Improved machine learning for image category recognition by local color constancy

Color constancy is the ability to recognize colors of objects invariant to the color of the light source. Systems for object detection or recognition in images use machine learning based on image descriptors to distinguish object and scene categories. However, there can be large variations in viewing and lighting conditions for real-world scenes, complicating the characteristics of images and consequently the image category recognition task. To reduce the effect of such variations, either color constancy algorithms or illumination-invariant color descriptors could be used. In this paper, we evaluate the performance of straightforward color constancy methods in practice, with respect to their utilization in a standard object classification problem, and also investigate their effects using local versions of these algorithms. These methods are then compared with color invariant descriptors. In a novel contribution, we ascertain that a combination of local color constancy methods and color invariant descriptors improve the performance of object recognition by as much as more than 10 percent, a significant improvement.