Effective and Efficient Image Copy Detection Based on GPU

Abstract

To improve the accuracy and efficiency of image copy detection, a novel system is proposed based on Graphics Processing Units (GPU). We combine two complementary local features, Harris-Laplace and SURF, to provide a compact representation of an image. By using complementary features, the image is better covered and the detection accuracy becomes less dependent on the actual image content. Moreover, ordinal measure (OM) is applied as semilocal spatial coherent verification. To improve time performance, the process of local features generation and OM calculating are implemented on the GPU through NVIDIA CUDA. Experiments show that our system achieves a 15% precision improvement over the baseline Hamming embedding approach. Compared to the CPU-based method, the GPU realization reaches up to a 30-40x speedup, having real-time performance.