Concurrent computation of connected pattern spectra for very large image information mining

This paper presents a shared-memory parallel algorithm for computing connected pattern spectra from the Max-Tree structure. The pattern spectrum is an aggregated feature space derived directly from the tree-based image representation and is a powerful tool for interactive image information mining. An application example along with timings on experiments with Gpixel input imagery are given. On images of 0.87 to 1.29 Gpixel, wall-clock times of 8.13 to 15.17s, and a speed up of between 27.5 and 33.5 were achieved on a single 2U 64 core rack server