Evaluation of Segmentation Quality via Adaptive Composition of Reference Segmentations

Evaluating image segmentation quality is a critical step for generating desirable segmented output and comparing performance of algorithms, among others. However, automatic evaluation of segmented results is inherently challenging since image segmentation is an ill-posed problem. This paper presents a framework to evaluate segmentation quality using multiple labeled segmentations which are considered as references. For a segmentation to be evaluated, we adaptively compose a reference segmentation using multiple labeled segmentations, which locally matches the input segments while preserving structural consistency. The quality of a given segmentation is then measured by its distance to the composed reference. A new dataset of 200 images, where each one has 6 to 15 labeled segmentations, is developed for performance evaluation of image segmentation. Furthermore, to quantitatively compare the proposed segmentation evaluation algorithm with the state-of-the-art methods, a benchmark segmentation evaluation dataset is proposed. Extensive experiments are carried out to validate the proposed segmentation evaluation framework.