Structure-measure: A New Way to Evaluate Foreground Maps
This work addresses the need for better evaluation metrics in salient object detection, which is crucial for assessing segmentation algorithms, though it is incremental as it builds on existing evaluation frameworks.
The paper tackled the problem of evaluating foreground maps in object segmentation by proposing a new structural similarity measure (Structure-measure) that addresses the limitation of existing pixel-wise error measures, and demonstrated its superiority over existing measures using 5 meta-measures on 5 benchmark datasets.
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.