Abstract
Local feature detection and description have gained a lot of interest in recent years since photometric descriptors computed for interest regions have proven to be very successful in many applications. In this paper, we propose a novel interest region descriptor which combines the strengths of the well-known SIFT descriptor and the LBP texture operator. It is called the center-symmetric local binary pattern (CS-LBP) descriptor. This new descriptor has several advantages such as tolerance to illumination changes, robustness on flat image areas, and computational efficiency. We evaluate our descriptor using a recently presented test protocol. Experimental results show that the CS-LBP descriptor outperforms the SIFT descriptor for most of the test cases, especially for images with severe illumination variations.
This is a preview of subscription content, log in via an institution to check access.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Mikolajczyk, K., Schmid, C.: Indexing based on scale invariant interest points. In: 8th IEEE International Conference on Computer Vision, vol. 1, pp. 525–531 (2001)
Tuytelaars, T., Gool, L.V.: Matching widely separated views based on affine invariant regions. International Journal of Computer Vision 59, 61–85 (2004)
Lowe, D.G.: Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, 91–110 (2004)
Lazebnik, S., Schmid, C., Ponce, J.: A sparse texture representation using local affine regions. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 1265–1278 (2005)
Se, S., Lowe, D., Little, J.: Global localization using distinctive visual features. In: IEEE/RSJ International Conference on Intelligent Robots and System, vol. 1, pp. 226–231 (2002)
Mikolajczyk, K., Tuytelaars, T., Schmid, C., Zisserman, A., Matas, J., Schaffalitzky, F., Kadir, T., Gool, L.V.: A comparison of affine region detectors. International Journal of Computer Vision 65, 43–72 (2005)
Mikolajczyk, K., Schmid, C.: A performance evaluation of local descriptors. IEEE Transactions on Pattern Analysis and Machine Intelligence 27, 1615–1630 (2005)
Bay, H., Tuytelaars, T., Gool, L.V.: SURF: Speeded up robust features. In: European Conference on Computer Vision, vol. 1, pp. 404–417 (2006)
Abdel-Hakim, A.E., Farag, A.A.: CSIFT: A SIFT descriptor with color invariant characteristics. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 2, pp. 1978–1983 (2006)
Brown, M., Szeliski, R., Winder, S.: Multi-image matching using multi-scale oriented patches. In: IEEE Computer Society Conference on Computer Vision and Pattern Recognition, vol. 1, pp. 510–517 (2005)
Ling, H., Jacobs, D.W.: Deformation invariant image matching. In: 10th IEEE International Conference on Computer Vision, vol. 2, pp. 1466–1473 (2005)
Ojala, T., Pietikäinen, M., Mäenpää, T.: Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence 24, 971–987 (2002)
Mikolajczyk, K., Schmid, C.: An affine invariant interest point detector. In: Heyden, A., Sparr, G., Nielsen, M., Johansen, P. (eds.) ECCV 2002. LNCS, vol. 2350, pp. 128–142. Springer, Heidelberg (2002)
Mikolajczyk, K., Schmid, C.: Scale & affine invariant interest point detectors. International Journal of Computer Vision 60, 63–86 (2004)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Heikkilä, M., Pietikäinen, M., Schmid, C. (2006). Description of Interest Regions with Center-Symmetric Local Binary Patterns. In: Kalra, P.K., Peleg, S. (eds) Computer Vision, Graphics and Image Processing. Lecture Notes in Computer Science, vol 4338. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11949619_6
Download citation
DOI: https://doi.org/10.1007/11949619_6
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-68301-8
Online ISBN: 978-3-540-68302-5
eBook Packages: Computer ScienceComputer Science (R0)