Parallelization and Optimization of SIFT Feature Extraction on Cluster System
نویسندگان
چکیده
Scale Invariant Feature Transform (SIFT) has been widely applied, but extracting SIFT feature is complicated and time-consuming. In this paper, to meet the demand of the real-time applications, SIFT is parallelized and optimized on cluster system, which is named pSIFT. Redundancy storage and communication are used for boundary data to improve the performance, and before representation of feature descriptor, data reallocation is adopted to keep load balance in pSIFT. Experimental results show that pSIFT achieves good speedup and scalability. Keywords—cluster, image matching, parallelization and optimization, SIFT.
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تاریخ انتشار 2012