A New Evaluation Framework and Image Dataset for Keypoint Extraction and Feature Descriptor Matching

Iñigo Barandiaran, Camilo Cortes, Marcos Nieto, Manuel Graña, Oscar E. Ruiz

Abstract

Key point extraction and description mechanisms play a crucial role in image matching, where several image points must be accurately identified to robustly estimate a transformation or to recognize an object or a scene. New procedures for keypoint extraction and for feature description are continuously emerging. In order to assess them accurately, normalized data and evaluation protocols are required. In response to these needs, we present a (1) new evaluation framework that allow assessing the performance of the state-of-the-art feature point extraction and description mechanisms, (2) a new image dataset acquired under controlled affine and photometric transformations and (3) a testing image generator. Our evaluation framework allows generating detailed curves about the performance of different approaches, providing a valuable insight about their behavior. Also, it can be easily integrated in many research and development environments. The contributions mentioned above are available on-line for the use of the scientific community.

References

  1. Alahi, A., Ortiz, R., and Vandergheynst, P. (2012). Freak: Fast retina keypoint. In IEEE Conference on Computer Vision and Pattern Recognition (To Appear).
  2. Bay, H., Tuytelaars, T., and Van Gool, L. (2006). Surf: Speeded up robust features. Computer Vision-ECCV 2006, pages 404-417.
  3. Bellavia, F., Tegolo, D., and Trucco, E. (2010). Improving sift-based descriptors stability to rotations. In Proceedings of the 2010 20th International Conference on Pattern Recognition, pages 3460-3463. IEEE Computer Society.
  4. Bradski, G. (2000). The OpenCV Library. Dr. Dobb's Journal of Software Tools.
  5. Fraundorfer, F. and Bischof, H. (2005). A novel performance evaluation method of local detectors on non-planar scenes. In Computer Vision and Pattern Recognition-Workshops, 2005. CVPR Workshops. IEEE Computer Society Conference on, pages 33-33. IEEE.
  6. Gauglitz, S., Höllerer, T., and Turk, M. (2011). Evaluation of interest point detectors and feature descriptors for visual tracking. International journal of computer vision, pages 1-26.
  7. Gil, A., Mozos, O., Ballesta, M., and Reinoso, O. (2010). A comparative evaluation of interest point detectors and local descriptors for visual slam. Machine Vision and Applications, 21(6):905-920.
  8. Hartley, R. I. and Zisserman, A. (2004). Multiple View Geometry in Computer Vision. Cambridge University Press, ISBN: 0521540518, second edition.
  9. Heikkilä, M., Pietikäinen, M., and Schmid, C. (2009). Description of interest regions with local binary patterns. Pattern Recognition, 42(3):425-436.
  10. Hughes, G. and Chraibi, M. (2011). Calculating ellipse overlap areas. arXiv preprint arXiv:1106.3787.
  11. Leutenegger, S., Chli, M., and Siegwart, R. (2011). Brisk: Binary robust invariant scalable keypoints. In Computer Vision (ICCV), 2011 IEEE International Conference on, pages 2548-2555. IEEE.
  12. Mikolajczyk, K. and Schmid, C. (2002). An affine invariant interest point detector. Computer Vision,ECCV 2002, pages 128-142.
  13. Mikolajczyk, K. and Schmid, C. (2005). A performance evaluation of local descriptors. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 27(10):1615-1630.
  14. Mikolajczyk, K., Tuytelaars, T., Schmid, C., et al. (2007). Affine covariant features. Collaborative work between: the Visual Geometry Group, Katholieke Universiteit Leuven, Inria Rhone-Alpes and the Center for Machine Perception.
  15. Moreels, P. and Perona, P. (2007). Evaluation of features detectors and descriptors based on 3d objects. International Journal of Computer Vision, 73(3):263-284.
  16. Tuytelaars, T. and Mikolajczyk, K. (2008). Local invariant feature detectors: a survey. Foundations and Trends R in Computer Graphics and Vision, 3(3):177-280.
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Paper Citation


in Harvard Style

Barandiaran I., Cortes C., Nieto M., Graña M. and Ruiz O. (2013). A New Evaluation Framework and Image Dataset for Keypoint Extraction and Feature Descriptor Matching . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-47-1, pages 252-257. DOI: 10.5220/0004211502520257


in Bibtex Style

@conference{visapp13,
author={Iñigo Barandiaran and Camilo Cortes and Marcos Nieto and Manuel Graña and Oscar E. Ruiz},
title={A New Evaluation Framework and Image Dataset for Keypoint Extraction and Feature Descriptor Matching},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={252-257},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004211502520257},
isbn={978-989-8565-47-1},
}


in EndNote Style

TY - CONF
JO - Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)
TI - A New Evaluation Framework and Image Dataset for Keypoint Extraction and Feature Descriptor Matching
SN - 978-989-8565-47-1
AU - Barandiaran I.
AU - Cortes C.
AU - Nieto M.
AU - Graña M.
AU - Ruiz O.
PY - 2013
SP - 252
EP - 257
DO - 10.5220/0004211502520257