Evaluation of Image Segmentation Quality by Adaptive Ground Truth Composition

Segmenting an image is an important step in many computer vision applications. However, image segmentation evaluation is far from being well-studied in contrast to the extensive studies on image segmentation algorithms. In this paper, we propose a framework to quantitatively evaluate the quality of a given segmentation with multiple ground truth segmentations. Instead of comparing directly the given segmentation to the ground truths, we assume that if a segmentation is “good”, it can be constructed by pieces of the ground truth segmentations. Then for a given segmentation, we construct adaptively a new ground truth which can be locally matched to the segmentation as much as possible and preserve the structural consistency in the ground truths. The quality of the segmentation can then be evaluated by measuring its distance to the adaptively composite ground truth. To the best of our knowledge, this is the first work that provides a framework to adaptively combine multiple ground truths for quantitative segmentation evaluation. Experiments are conducted on the benchmark Berkeley segmentation database, and the results show that the proposed method can faithfully reflect the perceptual qualities of segmentations.