Generic parallel data structures and algorithms to GPU superpixel image segmentation

Abstract

We propose parallel implementation on GPU (graphics processing unit) system for some generic algorithms applied to superpixel image segmentation problem. The aim is to provide standard algorithms based on generic decentralized data structures that could be easily improved and customized on many optimization problems on parallel platforms. Note that superpixel segmentation are clustering algorithms applied to image processing. Two types of algorithms are presented and implemented on GPU based on common parallel data structures. Firstly, we present a parallel implementation of the well-known k-means algorithm with application to 3D data. It is based on a cellular grid subdivision of space that allows closest point findings in constant optimal time for bounded distributions. Secondly, we present an application of the parallel Boruvka minimum spanning forest algorithm to compute watershed segmentation. Both techniques are fully executed on GPU and share the same data structures that embed disjoint-set-trees and distributed-link-lists. We evaluate our k-means approach with regards to state-of-the-art methods, that are, the well known SLIC algorithm, and the adaptive segmentation approach SPASM. We argue that our implementation has the shortest execution time among the tested methods, with a near real time performance and quasi linear acceleration factor, while it provides more regular shape superpixel segmentation based on hexagonal tessellation. Watershed minimum spanning forest method is presented and evaluated accordingly to the same experimental framework.