Quantitative Evaluation Metrics for Superpixel Segmentation

Superpixel segmentation methods have been found to be increasingly valuable in image processing and analysis. Superpixel segmentation approaches have been used as a preprocessing step for a wide variety of image analysis tasks such as full scene segmentation, automated scene understanding, object detection and classification, and have been used to reduce computation time during these tasks. While many quantitative evaluation metrics have been developed in the literature to analyze traditional image segmentation and clustering results, these metrics have not been used or adapted to quantitatively evaluate superpixel segmentations. In this paper, multiple superpixel segmentation algorithms are applied to synthetic aperture sonar (SAS) imagery and the results are evaluated using cluster validity indices that have been adapted for superpixel segmentation. Both cluster validity metrics that rely only on internal measures as well as those that use both internal and external measures are considered. Results are shown on a synthetic aperture sonar (SAS) data set.