The Improved SIFT Algorithm Based on Rectangular Operator and Its Parallel Implementation

Abstract

The SIFT algorithm is one of the most widely used algorithm which bases on local feature extraction. But it could not meet the requirement of the real-time application due to the high time complexity and low execution efficiency. In order to improve these drawback, the authors optimized the SIFT algorithm by using the Gaussian convolution scale of adaptive scale space. The authors also provided the executive process of the improved SIFT algorithm on the MapReduce programming model and compared its performance in terms of the stand-alone and cluster environment. The experiment result showed that compared to the traditional algorithm, the improved algorithm had high execution efficiency, good speedup, scalability and is suitable for massive amounts of image data processing.