GPU-based fast image copy detection

To speed up image copy detection by exploring the powerful computing capability of GPU, a novel GPU-based image copy detection scheme is proposed. Firstly, a new scale-invariant interest point detector-Harris-Hessian (H-H) is designed according to the architecture of GPU. The H-H extracts interest points in low scale and refines their location and scale in a series of scale-space with the determinant of Hessian matrix, which significantly reduces the pixel-level computation complexity and has better parallelism. Then, an image copy detection system based on the H-H is presented, the detection speed is significantly improved. The experimental results show that, compared to the existing CPU-based methods, the H-H achieves up to a speedup factor of 10~20 times and maintains a high detection accuracy. It only takes 19.8 ms for the system to detect a 640 × 480 image in a dataset of 11250 images with 95% accuracy rate, which meets the demand of real-time applications under large scale data.