Robust Histogram Construction from Color Invariants

A simple and effective object recognition scheme is to represent and match images on the basis of color histograms. To obtain robustness against varying imaging circumstances (e.g. a change in illumination, object pose, and viewpoint), color histograms are constructed from color invariants. However, in general, color invariants are negatively affected by sensor noise due to the instabilities of these color invariant transforms at many RGB values. To suppress the effect of noise blow-up for unstable color invariant values, in this paper, color invariant histograms are computed using variable kernel density estimators. To apply variable kernel density estimation in a principled way, models are proposed for the propagation of sensor noise through color invariants. As a result, the associated uncertainty is known for each color invariant value. The associated uncertainty is used to derive the parameterization of the variable kernel density estimator during histogram construction. It is empirically verified that the proposed method compares favorably to traditional color histograms for object recognition.