A Parallel Analysis on Scale Invariant Feature Transform (SIFT) Algorithm

Abstract

AbstractWith explosive growth of multimedia data on internet, the effective information retrieval from a large scale of multimedia data becomes more and more important. To retrieve these multimedia data automatically, some features in them must be extracted. Hence, image feature extraction algorithms have been a fundamental component of multimedia retrieval. Among these algorithms, Scale Invariant Feature Transform (SIFT) has been proven to be one of the most robust image feature extraction algorithm. However, SIFT algorithm is not only data intensive but also computation intensive. It takes about four seconds to process an image or a video frame on a general-purpose CPU, which is far from real-time processing requirement. Therefore, accelerating SIFT algorithm is urgently needed. As multi-core CPU becomes more and more popular in recent years, it is natural to employ computing power of multi-core CPU to accelerate SIFT. How to parallelize SIFT to take full use of multi-core capabilities becomes one of the core issues. This paper analyzes available parallelism in SIFT and implements various parallel SIFT algorithms to evaluate which is the most suitable for multi-core system. The final result shows that our parallel SIFT achieves a speedup of 10.46X on 16-core machine.KeywordsFeature PointParallel PerformanceScale Invariant Feature TransformMultimedia DataParallel SchemeThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.