Structure-measure: A new way to evaluate foreground maps
Proceedings of the IEEE international conference on computer …, 2017•openaccess.thecvf.com
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 …
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 …
Abstract
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.
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