A Comparative Study of SIFT and its Variants

Wu, Jian; Cui, Zhiming; Sheng, Victor S.; Zhao, Pengpeng; Su, Dongliang; Gong, Shengrong

doi:10.2478/msr-2013-0021

Abstract

SIFT is an image local feature description algorithm based on scale-space. Due to its strong matching ability, SIFT has many applications in different fields, such as image retrieval, image stitching, and machine vision. After SIFT was proposed, researchers have never stopped tuning it. The improved algorithms that have drawn a lot of attention are PCA-SIFT, GSIFT, CSIFT, SURF and ASIFT. In this paper, we first systematically analyze SIFT and its variants. Then, we evaluate their performance in different situations: scale change, rotation change, blur change, illumination change, and affine change. The experimental results show that each has its own advantages. SIFT and CSIFT perform the best under scale and rotation change. CSIFT improves SIFT under blur change and affine change, but not illumination change. GSIFT performs the best under blur change and illumination change. ASIFT performs the best under affine change. PCA-SIFT is always the second in different situations. SURF performs the worst in different situations, but runs the fastest.

References

[1] Ouyang, W., Tombari, F., Mattoccia, S., Di Stefano, L., Cham, W.-K. (2012). Performance evaluation of full search equivalent pattern matching algorithms. IEEETransactions on Pattern Analysis and MachineIntelligence, 34 (1), 127-143.10.1109/TPAMI.2011.10621576734
Search in Google Scholar
[2] Birinci, M., Diaz-de-Maria, F., Abdollahian, G. (2011). Neighborhood matching for object recognition algorithms based on local image features. In IEEEDigital Signal Processing Workshop and IEEE SignalProcessing Education Workshop (DSP/SPE), 4-7 January 2011. IEEE, 157-162.10.1109/DSP-SPE.2011.5739204
Search in Google Scholar
[3] Mian, A., Bennamoun, M., Owens, R. (2010). On the repeatability and quality of keypoints for local featurebased 3D object retrieval from cluttered scenes. International Journal of Computer Vision, 89 (2-3), 348-361.10.1007/s11263-009-0296-z
Search in Google Scholar
[4] Mikulka, J., Gescheidtova, E., Bartusek, K. (2012). Soft-tissues image processing: Comparison of traditional segmentation methods with 2D active contour methods. Measurement Science Review, 12 (4), 153-161.10.2478/v10048-012-0023-8
Search in Google Scholar
[5] Kim, D., Rho, S., Hwang, E. (2012). Local featurebased multi-object recognition scheme for surveillance. Engineering Applications of ArtificialIntelligence, 25 (7), 1373-1380.10.1016/j.engappai.2012.03.005
Search in Google Scholar
[6] Lowe, D.G. (1999). Object recognition from local scale invariant features. In Proceedings of the 7th IEEEInternational Conference on Computer Vision, 20-27 September 1999. IEEE, Vol. 2, 1150-1157.
Search in Google Scholar
[7] Lowe, D.G. (2004). Distinctive image features from scale-invariant keypoints. International Journal ofComputer Vision, 60 (2), 91-110.10.1023/B:VISI.0000029664.99615.94
Search in Google Scholar
[8] Tuytelaars, T., Mikolajczyk, K. (2008). Local invariant feature detectors: A survey. Foundations and Trendsin Computer Graphics and Vision, 3 (3), 177-280.
Search in Google Scholar
[9] Juan, L., Gwun, O. (2009). A comparison of SIFT, PCA-SIFT and SURF. International Journal of ImageProcessing, 3 (4), 143-152.
Search in Google Scholar
[10] Younes, L., Romaniuk, B., Bittar, E. (2012). A comprehensive and comparative survey of the SIFT algorithm - feature detection, description, and characterization. In Proceedings of the InternationalConference on Computer Vision Theory andApplications (VISAPP). SciTePress, Vol. 1, 467-474.
Search in Google Scholar
[11] Ke, Y., Sukthankar, R. (2004). PCA-SIFT: A more distinctive representation for local image descriptors. In Computer Vision and Pattern Recognition (CVPR2004), 27 June - 2 July 2004. IEEE, Vol. 2, 506-513.
Search in Google Scholar
[12] Mortensen, E.N., Deng, H., Shapiro, L. (2005). A SIFT descriptor with global context. In ComputerVision and Pattern Recognition (CVPR 2005), 20-25 June 2005. IEEE, Vol. 1, 184-190.
Search in Google Scholar
[13] Abdel-Hakim, A.E., Farag, A.A. (2006). CSIFT: A SIFT descriptor with color invariant characteristics. In Computer Vision and Pattern Recognition (CVPR2006), 17-22 June 2006. IEEE, Vol. 2, 1978-1983.
Search in Google Scholar
[14] Bay, H., Tuytelaars, T., Gool, L.V. (2006). SURF: Speeded up robust features. In Computer Vision -ECCV 2006 : 9th European Conference on ComputerVision, 7-13 May 2006. Springer, Part II, 404-417.10.1007/11744023_32
Search in Google Scholar
[15] Morel, J.M., Yu, G. (2009). ASIFT: A new framework for fully affine invariant image comparison. SIAMJournal on Imaging Sciences, 2 (2), 438-469.10.1137/080732730
Search in Google Scholar
[16] Rabbani, H. (2011). Statistical modeling of low SNR magnetic resonance images in wavelet domain using Laplacian prior and two-sided Rayleigh noise for visual quality improvement. Measurement ScienceReview, 11 (4), 125-130.10.2478/v10048-011-0023-0
Search in Google Scholar
[17] Benveniste, R., Unsalan, C. (2011). A color invariant for line stripe-based range scanners. The ComputerJournal, 54 (5), 738-753.10.1093/comjnl/bxq014
Search in Google Scholar
[18] Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A. (2005). A comparison of affine region detectors. International Journal of Computer Vision, 65 (1/2), 43-72.10.1007/s11263-005-3848-x
Search in Google Scholar
[19] Wu, Z., Radke, R.J. (2012). Using scene features to improve wide-area video surveillance. In ComputerVision and Pattern Recognition Workshops (CVPRW), 16-21 June 2012. IEEE, 50-57.10.1109/CVPRW.2012.6239206
Search in Google Scholar

A Comparative Study of SIFT and its Variants

Abstract

Paradigm

My account