On the parallel efficiency and scalability of the correntropy coefficient for image analysis

Abstract

Background: Similarity measures have application in many scenarios of digital image processing. The correntropy is a robust and relatively new similarity measure that recently has been employed in various engineering applications. Despite other competitive characteristics, its computational cost is relatively high and may impose hard-to-cope time restrictions for high-dimensional applications, including image analysis and computer vision. Methods: We propose a parallelization strategy for calculating the correntropy on multi-core architectures that may turn the use of this metric viable in such applications. We provide an analysis of its parallel efficiency and scalability. Results: The simulation results were obtained on a shared memory system with 24 processing cores for input images of different dimensions. We performed simulations of various scenarios with images of different sizes. The aim was to analyze the parallel and serial fraction of the computation of the correntropy coefficient and the influence of these fractions in its speedup and efficiency. Conclusions: The results indicate that correntropy has a large potential as a metric for image analysis in the multi-core era due to its high parallel efficiency and scalability.