SIFT implementation and optimization for multi-core systems

Scale invariant feature transform (SIFT) is an approach for extracting distinctive invariant features from images, and it has been successfully applied to many computer vision problems (e.g. face recognition and object detection). However, the SIFT feature extraction is compute-intensive, and a real-time or even super-real-time processing capability is required in many emerging scenarios. Nowadays, with the multi- core processor becoming mainstream, SIFT can be accelerated by fully utilizing the computing power of available multi-core processors. In this paper, we propose two parallel SIFT algorithms and present some optimization techniques to improve the implementation ´s performance on multi-core systems. The result shows our improved parallel SIFT implementation can process general video images in super-real-time on a dual-socket, quad-core system, and the speed is much faster than the implementation on GPUs. We also conduct a detailed scalability and memory performance analysison the 8-core system and on a 32-core chip multiprocessor (CMP) simulator. The analysis helps us identify possible causes of bottlenecks, and we suggest avenues for scalability improvement to make this application more powerful on future large-scale multi- core systems.