Over- and under-segmentation evaluation based on the segmentation covering measure

Abstract

Very few measures intended for evaluating the quality of image segmentations account separately for over- and under-segmentation. This distinction is highly desirable in practice because in many applications undersegmentation is considered as a much serious issue than over-segmentation. In this paper, a new approach to this problem is presented as a decomposition of the Segmentation Covering measure into two contributions, one due to over-segmentation and the other one to under-segmentation. Our proposal has been tested on the output of state-of-the-art segmentation algorithms using the Berkeley image database. The results obtained are comparable to those provided by similar evaluation methods allowing a clear separation between over- and undersegmentation effects.