Parallelization and Optimization of SIFT Feature Extraction on Cluster System
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.
[1] D. G. Lowe, "Distinctive image features from scale-invariant keypoints,"
International Journal of Computer Vision, vol. 60, pp. 91-110, 2004.
[2] A.Y.Ke and R.Sukthankar, "PCA-SIFT: A more distinctive
representation for local image descriptors," In Proc. 2004 IEEE Computer
Society Conference on Computer Vision and Pattern Recognition
(CVPR-04), pp.506-513.
[3] Mikolajczyk, K., Schmid, C., "A performance evaluation of local
descriptors," IEEE Trans. Pattern Analysis and Machine Intelligence.
Vol.27, pp.1615-1630, Augst 2005.
[4] Alaa E. Abdel-Hakim and Aly A. Farag, "CSIFT: A SIFT Descriptor with
Color Invariant Characteristics," in proc. 2006 IEEE Computer Society
Conference on Computer Vision and Pattern Recognition (CVPR-06).
[5] Geoffrey Treen, and Anthony Whitehead, "Efficient SIFT Matching from
Keypoint Descriptor Properties," 2009 Workshop on Applications of
Computer Vision(WACV), pp1-7.
[6] Vanderlei Bonato, Eduardo Marques, and George A. Constantinides, "A
Parallel Hardware Architecture for Scale and Rotation Invariant Feature
Detection," IEEE Trans. Circuits and Systems for Video Technology,
VOL.18, pp1703-1712, 2008.
[7] Seth Warn, Wesley Emeneker , Jackson Cothren,Amy Apon,
"Accelerating SIFT on Parallel Architectures," In Proc. 2009 IEEE Int.
Conf. Cluster Computing and Workshops(CLUSTER-09), pp.1-4.
[8] Marc Lalonde, David Byrns, Langis Gagnon, Normand Teasdale, Denis
Laurendeau, "Real-time eye blink detection with GPU-based SIFT
tracking," In Proc. 4th Canadian Conference on Computer and Robot
Vision(CRV'07), pp.481-487,2007.
[9] Sirmacek, B., Unsalan, C., "Urban-Area and Building Detection Using
SIFT Keypoints and Graph Theory," IEEE Trans. Geoscience and
Remote Sensing, Vol.47, pp.1156-1167, 2009.
[10] Gangqiang Zhao, Ling Chen, Jie Song, Gencai Chen, "Large head
movement tracking using SIFT-based registration," In Proc. 15th
international conference on Multimedia, PP: 807-810, 2007.
[11] Jiang, R.M., Crookes, D., Luo, N., Davidson, M.W., "Live-Cell Tracking
Using SIFT Features in DIC Microscopic Videos," IEEE Trans.
Biomedical Engineering, Vol.57, pp: 2219-2228, 2010.
[12] Goncalves, H., Corte-Real, L., Goncalves, J.A., "Automatic Image
Registration through Image Segmentation and SIFT," IEEE Trans.
Geoscience and Remote Sensing, Vol.49 pp.2589-2600, 2011.
[13] Yi, Z., Zhiguo, C., Yang, X., "Multi-spectral remote image registration
based on SIFT," IEEE Electronics Letters, Vol.44 pp. 107-108,2008.
[14] Sudipta N. Sinha, Jan-Michael Frahm, Marc Pollefeys, and Yakup Genc,
"Feature Tracking and Matching in Video Using Programmable Graphics
Hardware," Machine Vision and Applications, Vol.22, pp.207-217,
March 2007.
[15] S. Heymann, K. Muller, A. Smolic, B. Froehlich, and T. Wiegand, "SIFT
implementation and optimization for general-purpose GPU," In Proc.
WSCG-07, 2007.
[16] Q. Zhang, Y. Chen, Y. Zhang, and Y. Xu, "Sift implementation and
optimization for multi-core systems," IEEE International Symposium on
Parallel and Distributed Processing (IPDPS 2008), pp. 1-8, 2008.
[17] H. Feng, E. Li, Y. Chen, and Y. Zhang, "Parallelization and
characterization of sift on multi-core systems," IEEE International
Symposium on Workload Characterization (IISWC-08), pp. 14-23, 2008.
[18] Guiyuan Jiang, Guiling Zhang and Dakun Zhang, "A Distributed
Dynamic Parallel Algorithm for SIFT Feature Extraction," 3rd
International Symposium on Parallel Architectures, Algorithms and
Programming (PAAP), pp.381-385, 2010.
[19] Lowe, D.G., "Object recognition from local scale-invariant features," In
Proc. IEEE Int Conf. Computer Vision, pp. 1150-1157, 1999.
[20] Andrea Vedaldi, SIFT source code, download from
http://www.vlfeat.org/~vedaldi/assets/ siftpp/versions/.
[1] D. G. Lowe, "Distinctive image features from scale-invariant keypoints,"
International Journal of Computer Vision, vol. 60, pp. 91-110, 2004.
[2] A.Y.Ke and R.Sukthankar, "PCA-SIFT: A more distinctive
representation for local image descriptors," In Proc. 2004 IEEE Computer
Society Conference on Computer Vision and Pattern Recognition
(CVPR-04), pp.506-513.
[3] Mikolajczyk, K., Schmid, C., "A performance evaluation of local
descriptors," IEEE Trans. Pattern Analysis and Machine Intelligence.
Vol.27, pp.1615-1630, Augst 2005.
[4] Alaa E. Abdel-Hakim and Aly A. Farag, "CSIFT: A SIFT Descriptor with
Color Invariant Characteristics," in proc. 2006 IEEE Computer Society
Conference on Computer Vision and Pattern Recognition (CVPR-06).
[5] Geoffrey Treen, and Anthony Whitehead, "Efficient SIFT Matching from
Keypoint Descriptor Properties," 2009 Workshop on Applications of
Computer Vision(WACV), pp1-7.
[6] Vanderlei Bonato, Eduardo Marques, and George A. Constantinides, "A
Parallel Hardware Architecture for Scale and Rotation Invariant Feature
Detection," IEEE Trans. Circuits and Systems for Video Technology,
VOL.18, pp1703-1712, 2008.
[7] Seth Warn, Wesley Emeneker , Jackson Cothren,Amy Apon,
"Accelerating SIFT on Parallel Architectures," In Proc. 2009 IEEE Int.
Conf. Cluster Computing and Workshops(CLUSTER-09), pp.1-4.
[8] Marc Lalonde, David Byrns, Langis Gagnon, Normand Teasdale, Denis
Laurendeau, "Real-time eye blink detection with GPU-based SIFT
tracking," In Proc. 4th Canadian Conference on Computer and Robot
Vision(CRV'07), pp.481-487,2007.
[9] Sirmacek, B., Unsalan, C., "Urban-Area and Building Detection Using
SIFT Keypoints and Graph Theory," IEEE Trans. Geoscience and
Remote Sensing, Vol.47, pp.1156-1167, 2009.
[10] Gangqiang Zhao, Ling Chen, Jie Song, Gencai Chen, "Large head
movement tracking using SIFT-based registration," In Proc. 15th
international conference on Multimedia, PP: 807-810, 2007.
[11] Jiang, R.M., Crookes, D., Luo, N., Davidson, M.W., "Live-Cell Tracking
Using SIFT Features in DIC Microscopic Videos," IEEE Trans.
Biomedical Engineering, Vol.57, pp: 2219-2228, 2010.
[12] Goncalves, H., Corte-Real, L., Goncalves, J.A., "Automatic Image
Registration through Image Segmentation and SIFT," IEEE Trans.
Geoscience and Remote Sensing, Vol.49 pp.2589-2600, 2011.
[13] Yi, Z., Zhiguo, C., Yang, X., "Multi-spectral remote image registration
based on SIFT," IEEE Electronics Letters, Vol.44 pp. 107-108,2008.
[14] Sudipta N. Sinha, Jan-Michael Frahm, Marc Pollefeys, and Yakup Genc,
"Feature Tracking and Matching in Video Using Programmable Graphics
Hardware," Machine Vision and Applications, Vol.22, pp.207-217,
March 2007.
[15] S. Heymann, K. Muller, A. Smolic, B. Froehlich, and T. Wiegand, "SIFT
implementation and optimization for general-purpose GPU," In Proc.
WSCG-07, 2007.
[16] Q. Zhang, Y. Chen, Y. Zhang, and Y. Xu, "Sift implementation and
optimization for multi-core systems," IEEE International Symposium on
Parallel and Distributed Processing (IPDPS 2008), pp. 1-8, 2008.
[17] H. Feng, E. Li, Y. Chen, and Y. Zhang, "Parallelization and
characterization of sift on multi-core systems," IEEE International
Symposium on Workload Characterization (IISWC-08), pp. 14-23, 2008.
[18] Guiyuan Jiang, Guiling Zhang and Dakun Zhang, "A Distributed
Dynamic Parallel Algorithm for SIFT Feature Extraction," 3rd
International Symposium on Parallel Architectures, Algorithms and
Programming (PAAP), pp.381-385, 2010.
[19] Lowe, D.G., "Object recognition from local scale-invariant features," In
Proc. IEEE Int Conf. Computer Vision, pp. 1150-1157, 1999.
[20] Andrea Vedaldi, SIFT source code, download from
http://www.vlfeat.org/~vedaldi/assets/ siftpp/versions/.
@article{"International Journal of Information, Control and Computer Sciences:63965", author = "Mingling Zheng and Zhenlong Song and Ke Xu and Hengzhu Liu", title = "Parallelization and Optimization of SIFT Feature Extraction on Cluster System", abstract = "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.", volume = "6", number = "4", pages = "522-5", }
