Superpixels by Bilateral Geodesic Distance

We present a novel superpixel generation algorithm based on a new definition of geodesic distance, called bilateral geodesic distance. In contrast to the traditional geodesic distance, the new bilateral geodesic distance of two pixels considers the distance between their positions as well as their color difference. Superpixel generation is essentially a problem of clustering image pixels with respect to a set of properly selected seeds. We first use an adaptive hexagonal subdivision method to determine the initial seed-based image gradient. Then, we use the bilateral geodesic distance to measure the similarity between the pixels and the seeds. We apply an improved fast marching method to generate superpixels’ contour regions with the expansion velocities dependent on a new gradient formulation that depends on the seeds’ properties. The experimental results indicate that our algorithm is not only much faster than the structure-based method, which uses conventional geodesic distance, but also outperforms the existing methods in terms of region compactness and region boundary regularity.