SIFT 와 SURF 알고리즘의 성능적 비교 분석

Comparative Analysis of the Performance of SIFT and SURF

  • 이용환 (단국대학교 응용컴퓨터공학과) ;
  • 박제호 (단국대학교 컴퓨터과학과) ;
  • 김영섭 (단국대학교 전자공학과)
  • Lee, Yong-Hwan (Department of Applied Computer Engineering, Dankook University) ;
  • Park, Je-Ho (Dept. of Computer Science, Dankook University) ;
  • Kim, Youngseop (Dept. of Electronic Engineering, Dankook University)
  • 투고 : 2013.09.03
  • 심사 : 2013.09.23
  • 발행 : 2013.09.30


Accurate and robust image registration is important task in many applications such as image retrieval and computer vision. To perform the image registration, essential required steps are needed in the process: feature detection, extraction, matching, and reconstruction of image. In the process of these function, feature extraction not only plays a key role, but also have a big effect on its performance. There are two representative algorithms for extracting image features, which are scale invariant feature transform (SIFT) and speeded up robust feature (SURF). In this paper, we present and evaluate two methods, focusing on comparative analysis of the performance. Experiments for accurate and robust feature detection are shown on various environments such like scale changes, rotation and affine transformation. Experimental trials revealed that SURF algorithm exhibited a significant result in both extracting feature points and matching time, compared to SIFT method.



연구 과제 주관 기관 : 한국콘텐츠진흥원


  1. Ethan Rublee, Vincent Rabaud, Kurt Konolige, Gary Bradski, "ORB: an efficient alternative to SIFT or SURF", International Conference on Computer Vision, pp.2564-2571, 2011.
  2. B.S. Manjunath, Jens-Rainer Ohm, Vinod V. Vasudevan, Akio Yamada, "Color and Texture Descriptors", IEEE Transactions on Circuits and Systems for Video Technology, vol.11, no.6, 2001.
  3. Jun Yang, Shi-jiao Zhu, "Narrowing Semantic Gap in Content-based Image Retrieval", International Conference on Computer Distributed Control and Intelligent Environmental Monitoring, pp.433-438, 2012.
  4. Ran Tao, "Visual Concept Detection and Real Time Object Detection", Computer Vision and Pattern Recognition, 2011.
  5. David G. Lowe, "Distinctive Image Features from Scale-Invariant Keypoints", International Journal of Computer Vision, vol.60, issue.2, pp.91-110, 2004.
  6. Herbert Bay, Tinne Tuytelaars, Luc Van Gool, "SURF: Speeded Up Robust Features", Computer Vision and Image Understanding, vol.110, no.3, pp.346-359, 2008.
  7. Paul Viola, Michael Jones. "Rapid Object Detection using a Boosted Cascade of Simple Features", Conference on Computer Vision and Pattern Recognition, vol.1, pp.511-518, 2001.
  8. Rong Zhao, William I. Grosky, "Chapter 2. Bridging the Semantic Gap in Image Retrieval", Distributed Multimedia Databases: Techniques and Applications, Idea Group Publishing, pp.14-36,
  9. Luo Juan, Oubong Gwun, "A Comparison of SIFT, CA-SIFT and SURF", International Journal of Image Processing, vol.3, issue.4, pp.143-152, 2011.
  10. Website
  11. Website