Edge Adaptive Seeding for Superpixel Segmentation

Christian Wilms and Simone Frintrop

GCPR 2017

Finding a suitable seeding resolution when using superpixel segmentation methods is usually challenging. Different parts of the image contain different levels of clutter, resulting in an either too dense or too coarse segmentation. Since both possible solutions cause problems with respect to subsequent processing, we propose an edge adaptive seeding for superpixel segmentation methods, generating more seeds in areas with more edges and vise versa. This follows the assumption that edges distinguish objects and thus are a good indicator of the level of clutter in an image region. We show in our evaluation on five datasets by using three popular superpixel segmentation methods that using edge adaptive seeding leads to improved results compared to other priors as well as to uniform seeding.