Efficient Image Re-Ranking Computation on GPUs

The huge growth of image collections and multimedia resources available is remarkable. One of the most common approaches to support image searches relies on the use of Content-Based Image Retrieval (CBIR) systems. CBIR systems aim at retrieving the most similar images in a collection, given a query image. Since the effectiveness of those systems is very dependent on the accuracy of ranking approaches, re-ranking algorithms have been proposed to exploit contextual information and improve the effectiveness of CBIR systems. Image re-ranking algorithms typically consider the relationship among every image in a given dataset when computing the new ranking. This approach demands a huge amount of computational power, which may render it prohibitive on very large data sets. In order to mitigate this problem, we propose using the computational power of Graphics Processing Units (GPU) to speedup the computation of image re-ranking algorithms. GPUs are fast emerging and relatively inexpensive parallel processors that are becoming available on a wide range of computer systems. In this paper, we propose a parallel implementation of an image re-ranking algorithm designed to fit the computational model of GPUs. Experimental results demonstrate that relevant performance gains can be obtained by our approach.