A Parallel Analysis on Scale Invariant Feature Transform (SIFT) Algorithm
With explosive growth of multimedia data on internet, the e.ective 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 .nal result shows that our parallel SIFT achieves a speedup of 10.46X on 16-core machine.
Donglei Yang Lili Liu Feiwen Zhu Weihua Zhang
Parallel Processing Institute, Fudan University
国际会议
上海
英文
98-111
2011-09-26（万方平台首次上网日期，不代表论文的发表时间）