Spherical Superpixel Segmentation

These days, superpixel algorithms are widely used in computer vision and multimedia applications. However, existing algorithms are designed for planar images, which are less suited to deal with wide angle images. In this paper, we present a superpixel segmentation method for $\text{360}^\circ$ spherical images. Unlike previous methods, our approach explicitly considers the geometry for spherical images and makes clustering to spherical image pixels. It starts with the seeds defined by Hammersley points sampled on the sphere, then iterates between assignment step and update step, which are both based on the distance metric respecting spherical geometry. We evaluate our method on the transformed Berkeley segmentation dataset and panorama segmentation dataset collected by ourselves. Experimental results show that our method can gain better performance in terms of adherence to image boundaries and superpixel structural regularity. Furthermore, superpixels generated by our method can reserve the coherence across image boundaries and all have closed contours.