GPU implementation of MCE approach to finding near neighbourhoods

Abstract

This paper presents a parallel version of the Maximal Clique Enumeration (MCE) approach for discovering tolerance classes. Finding such classes is a computationally complex problem, especially in the case of large data sets or in content-based retrieval applications(CBIR). The GPU implementation is an extension of earlier work by the authors on finding efficient methods for computing tolerance classes in images. The experimental results demonstrate that the GPU-based MCE algorithm is faster than the serial MCE implementation and can perform computations with higher values of tolerance ε. © 2013 Springer-Verlag.