Extracting Scale and Illuminant Invariant Regions Through Color

Abstract

Despite the fact that color is a powerful cue in object recognition, the extraction of scale-invariant interest regions from color images frequently begins with a conversion of the image to grayscale. The isolation of interest points is then completely determined by luminance, and the use of color is deferred to the stage of descriptor formation. This seemingly innocuous conversion to grayscale is known to suppress saliency and can lead to representative regions being undetected by procedures based only on luminance. Furthermore, grayscaled images of the same scene under even slightly different illuminants can appear sufficiently different as to affect the repeatability of detections across images. We propose a method that combines information from the color channels to drive the detection of scale-invariant keypoints. By factoring out the local effect of the illuminant using an expressive linear model, we demonstrate robustness to a change in the illuminant without having to estimate its properties from the image. Results are shown on challenging images from two commonly used color constancy datasets.