Speeding Up SIFT Algorithm by Multi-core Processor Supporting SIMD Instruction Sets

Scale Invariant Feature Transform (SIFT) method plays a critical role in a wide variety of vision applications. But it is now facing the real-time computational challenge. Parallel computing is one of the most promising solutions to overcome the computational challenge. In this paper, we target at parallelizing SIFT by multi-core architecture with per-core SIMD support. We focus on the SIMDization of data parallel parts of SIFT to fully utilize per-core computing power. At Orientation Assignment and Key point Descriptor stages, we observe that load balance is an important factor. We also implement the optimized algorithm on multi-core system with SIMD support from Tianhe-2 Supercomputer and make comparison with the State-of-the-Art parallel SIFT algorithms.