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.
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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)
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© 2006 Springer-Verlag Berlin Heidelberg
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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
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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
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