Parallelizing image feature extraction algorithms on multi-core platforms

Abstract

Currently, multimedia data has become one of the most important data types processed and transferred over the Internet. To extract useful information from a huge amount of such data, SIFT and SURF, as two most popular image feature extraction algorithms, have been widely used in many applications running on multi-core platforms. However, limited parallelism in existing designs makes it hard or impossible to apply them in many applications with real-time requirements. Therefore, it has become one of the major challenges to improve the processing speed of image feature extraction algorithms.In this paper, we first analyze the parallelism constraints in the algorithms, such as imbalanced workloads and indeterminate time distributions. Based on such analyses, we present an adaptive pipeline parallel scheme (AD-PIPE) to adjust the thread number in different stages according to their workloads dynamically, which achieves a balanced partition for constant input workloads. Furthermore, we also implement a power efficient version (AE-PIPE) for AD-PIPE through scheduling threads based on variable input workloads. Experimental results show that AD-PIPE achieves a speedup of 16.88X and 20.33X respectively over SIFT and SURF on a 16-core machine. Moreover, AE-PIPE achieves up to 52.94% and 58.82% power saving with only 3% performance loss.