Abstract: In this paper, we address the problem of shadow detection and removal from single images of natural scenes.
Different from traditional methods that explore pixel or edge information, we employ a region based approach. In addition to
considering individual regions separately, we predict relative illumination conditions between segmented regions from their
appearances and perform pairwise classification based on such information. Classification results are used to build a graph of
segments, and graph-cut is used to solve the labeling of shadow and non-shadow regions. Detection results are later refined by
image matting, and the shadow free image is recovered by relighting each pixel based on our lighting model. We evaluate our
method on the shadow detection dataset in . In addition, we created a new dataset with shadow-free ground truth images,
which provides a quantitative basis for evaluating shadow removal.