%0 Journal Article
	%A Mingling Zheng and  Zhenlong Song and  Ke Xu and  Hengzhu Liu
	%D 2012
	%J International Journal of Computer and Information Engineering
	%B World Academy of Science, Engineering and Technology
	%I Open Science Index 64, 2012
	%T Parallelization and Optimization of SIFT Feature Extraction on Cluster System
	%U https://publications.waset.org/pdf/14966
	%V 64
	%X 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.
	%P 461 - 465