Accelerating Content-Based Image Retrieval via GPU-Adaptive Index Structure

A tremendous amount of work has been conducted in content-based image retrieval (CBIR) on designing effective index structure to accelerate the retrieval process. Most of them improve the retrieval efficiency via complex index structures, and few take into account the parallel implementation of them on underlying hardware, making the existing index structures suffer from low-degree of parallelism. In this paper, a novel graphics processing unit (GPU) adaptive index structure, termed as plane semantic ball (PSB), is proposed to simultaneously reduce the work of retrieval process and exploit the parallel acceleration of underlying hardware. In PSB, semantics are embedded into the generation of representative pivots and multiple balls are selected to cover more informative reference features. With PSB, the online retrieval of CBIR is factorized into independent components that are implemented on GPU efficiently. Comparative experiments with GPU-based brute force approach demonstrate that the proposed approach can achieve high speedup with little information loss. Furthermore, PSB is compared with the state-of-the-art approach, random ball cover (RBC), on two standard image datasets, Corel 10 K and GIST 1 M. Experimental results show that our approach achieves higher speedup than RBC on the same accuracy level.