Sampled Multi-scale Color Local Binary Patterns
Yu Zhang, Stéphane Bres, Liming Chen
2013
Abstract
In this paper, we propose a novel representation, called sampled multi-scale color Local Binary Pattern (SMCLBP), and apply it to Visual Object Classes (VOC) Recognition. The Local Binary Pattern (LBP) has been proven to be effective for image representation, but it is too local to be robust. Meanwhile such a design cannot fully exploit the discriminative capacity of the features available and deal with various changes in lighting and viewing conditions in real-world scenes. In order to address these problems, we propose SMC-LBP, which randomly samples the neighboring pixels across different scale circles, instead of pixels from individual circular in the original LBP scheme. The proposed descriptor presents several advantages: (1) It encodes not only single scale but also multiple scales of image patterns, and hence provides a more complete image information than the original LBP descriptor; (2) It cooperates with color information, therefore its photometric invariance property and discriminative power is enhanced. The experimental results on the PASCAL VOC 2007 image benchmark show significant accuracy improvement by the proposed descriptor compared with both the original LBP and other popular texture descriptors.
References
- Blas, M. R., Agrawal, M., Sundaresan, A., and Konolige, K. (2008). Fast color/texture segmentation for outdoor robots. In IROS, pages 4078-4085.
- Calonder, M., Lepetit, V., Strecha, C., and Fua, P. (2010). Brief: binary robust independent elementary features. In Proceedings of the 11th European conference on Computer vision: Part IV, ECCV'10, pages 778-792, Berlin, Heidelberg. Springer-Verlag.
- Caputo, B., Hayman, E., and Mallikarjuna, P. (2005). Classspecific material categorisation. IEEE International Conference on Computer Vision (ICCV), 2:1597- 1604.
- Finlayson, G. D., Hordley, S. D., and Xu, R. (2005). Convex programming colour constancy with a diagonal-offset model. In ICIP (3)7805, pages 948-951.
- Guo, Z., 0006, L. Z., Zhang, D., and Mou, X. (2010). Hierarchical multiscale lbp for face and palmprint recognition. In ICIP, pages 4521-4524. IEEE.
- Liao, S., Zhu, X., Lei, Z., Zhang, L., and Li, S. Z. (2007). Learning multi-scale block local binary patterns for face recognition. In Lee, S.-W. and Li, S. Z., editors, ICB, volume 4642 of Lecture Notes in Computer Science, pages 828-837. Springer.
- Lowe, D. G. (2004). Distinctive image features from scaleinvariant keypoints. Int. J. Comput. Vision, 60(2):91- 110.
- Ojala, T., Pietikäinen, M., and Mäenpää, T. (2002). Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans. Pattern Anal. Mach. Intell., 24(7):971-987.
- Ozuysal, M., Calonder, M., Lepetit, V., and Fua, P. (2010). Fast keypoint recognition using random ferns. IEEE Trans. Pattern Anal. Mach. Intell., 32(3):448-461.
- Shan, C., Gong, S., and McOwan, P. W. (2009). Facial expression recognition based on local binary patterns: A comprehensive study. Image Vision Comput., 27(6):803-816.
- Tuceryan, M. and Jain, A. K. (1998). Texture analysis. In Handbook of Pattern Recognition and Computer Vision, pages 235-276.
- van de Sande, K. E. A., Gevers, T., and Snoek, C. G. M. (2010). Evaluating color descriptors for object and scene recognition. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32(9):1582-1596.
- Yue, Y., Finley, T., Radlinski, F., and Joachims, T. (2007). A support vector method for optimizing average precision. In Proceedings of the 30th annual international ACM SIGIR conference on Research and development in information retrieval, SIGIR 7807, pages 271-278, New York, NY, USA. ACM.
- Zhang, D., Wong, A., Indrawan, M., and Lu, G. (2000). Content-based image retrieval using gabor texture features. In IEEE Transactions PAMI, pages 13-15.
- Zhu, C., Bichot, C.-E., and Chen, L. (2010). Multi-scale Color Local Binary Patterns for Visual Object Classes Recognition. In IEEE, editor, International Conference on Pattern Recognition (ICPR), pages 3065- 3068.
Paper Citation
in Harvard Style
Zhang Y., Bres S. and Chen L. (2013). Sampled Multi-scale Color Local Binary Patterns . In Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013) ISBN 978-989-8565-47-1, pages 303-308. DOI: 10.5220/0004282403030308
in Bibtex Style
@conference{visapp13,
author={Yu Zhang and Stéphane Bres and Liming Chen},
title={Sampled Multi-scale Color Local Binary Patterns},
booktitle={Proceedings of the International Conference on Computer Vision Theory and Applications - Volume 1: VISAPP, (VISIGRAPP 2013)},
year={2013},
pages={303-308},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004282403030308},
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 - Sampled Multi-scale Color Local Binary Patterns
SN - 978-989-8565-47-1
AU - Zhang Y.
AU - Bres S.
AU - Chen L.
PY - 2013
SP - 303
EP - 308
DO - 10.5220/0004282403030308