  Foreground map evaluation is crucial for gauging the progress of object
segmentation algorithms, in particular in the filed of salient object detection
where the purpose is to accurately detect and segment the most salient object
in a scene. Several widely-used measures such as Area Under the Curve (AUC),
Average Precision (AP) and the recently proposed Fbw have been utilized to
evaluate the similarity between a non-binary saliency map (SM) and a
ground-truth (GT) map. These measures are based on pixel-wise errors and often
ignore the structural similarities. Behavioral vision studies, however, have
shown that the human visual system is highly sensitive to structures in scenes.
Here, we propose a novel, efficient, and easy to calculate measure known an
structural similarity measure (Structure-measure) to evaluate non-binary
foreground maps. Our new measure simultaneously evaluates region-aware and
object-aware structural similarity between a SM and a GT map. We demonstrate
superiority of our measure over existing ones using 5 meta-measures on 5
benchmark datasets.
