Hierarchical Energy-transfer Features

Radovan Fusek, Eduard Sojka, Karel Mozdřeň, Milan Šurkala

2014

Abstract

In the paper, we propose the novel and efficient object descriptors that are designed to describe the appearance of the objects. The descriptors are called as Hierarchical Energy-Transfer Features (HETF). The main idea behind HETF is that the shape of the objects can be described by the function of energy distribution. In the image, the transfer of energy is solved by making use of physical laws. The function of the energy distribution is obtained by sampling, after the energy transfer process; the image is divided into the cells of variable sizes and the values of the function is investigated inside each cell. The proposed descriptors achieved very good detection results compared with the state-of-the-art methods (e.g. Haar, HOG, LBP features). We show the robustness of the descriptors for solving the face detection problem.

References

  1. Berg, T. L., Berg, A. C., Edwards, J., and Forsyth, D. (2005). Who's in the picture. In Saul, L. K., Weiss, Y., and Bottou, L., editors, Advances in Neural Information Processing Systems 17, pages 137-144. MIT Press, Cambridge, MA.
  2. Bosch, A., Zisserman, A., and Munoz, X. (2007). Representing shape with a spatial pyramid kernel. In Proceedings of the 6th ACM international conference on Image and video retrieval, CIVR 7807, pages 401-408, New York, NY, USA. ACM.
  3. Boser, B. E., Guyon, I. M., and Vapnik, V. N. (1992). A training algorithm for optimal margin classifiers. In Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, pages 144-152. ACM Press.
  4. Dalal, N. and Triggs, B. (2005). Histograms of oriented gradients for human detection. In Computer Vision and Pattern Recognition, 2005. CVPR 2005. IEEE Computer Society Conference on, volume 1, pages 886 - 893 vol. 1.
  5. Felzenszwalb, P., Girshick, R., McAllester, D., and Ramanan, D. (2010). Object detection with discriminatively trained part-based models. Pattern Analysis and Machine Intelligence, IEEE Transactions on, 32(9):1627-1645.
  6. Ferrari, V., Marin-Jimenez, M., and Zisserman, A. (2008). Progressive search space reduction for human pose estimation. In Computer Vision and Pattern Recognition, 2008. CVPR 2008. IEEE Conference on, pages 1-8.
  7. Freund, Y. and Schapire, R. E. (1995). A decision-theoretic generalization of on-line learning and an application to boosting. In Proceedings of the Second European Conference on Computational Learning Theory, EuroCOLT 7895, pages 23-37, London, UK, UK. Springer-Verlag.
  8. Fusek, R., Sojka, E., Mozdren, K., and Surkala, M. (2013). Energy-transfer features and their application in the task of face detection. In Advanced Video and Signal Based Surveillance (AVSS), 2013 10th IEEE International Conference on, pages 147-152.
  9. Hadid, A., Pietikainen, M., and Ahonen, T. (2004). A discriminative feature space for detecting and recognizing faces. In Computer Vision and Pattern Recognition, 2004. CVPR 2004. Proceedings of the 2004 IEEE Computer Society Conference on, volume 2, pages II797-II-804 Vol.2.
  10. Lee, K., Ho, J., and Kriegman, D. (2005). Acquiring linear subspaces for face recognition under variable lighting. IEEE Trans. Pattern Anal. Mach. Intelligence, 27(5):684-698.
  11. Liao, S., Zhu, X., Lei, Z., Zhang, L., and Li, S. Z. (2007). Learning multi-scale block local binary patterns for face recognition. In ICB, pages 828-837.
  12. Lienhart, R. and Maydt, J. (2002). An extended set of haarlike features for rapid object detection. In Image Processing. 2002. Proceedings. 2002 International Conference on, volume 1, pages I-900-I-903 vol.1.
  13. Ojala, T., Pietikäinen, M., and Harwood, D. (1996). A comparative study of texture measures with classification based on featured distributions. Pattern Recognition, 29(1):51-59.
  14. Papageorgiou, C. and Poggio, T. (2000). A trainable system for object detection. Int. J. Comput. Vision, 38(1):15- 33.
  15. Perona, P. and Malik, J. (1990). Scale-space and edge detection using anisotropic diffusion. IEEE Trans. Pattern Anal. Mach. Intell., 12:629-639.
  16. Tan, X. and Triggs, B. (2010). Enhanced local texture feature sets for face recognition under difficult lighting conditions. Image Processing, IEEE Transactions on, 19(6):1635-1650.
  17. Viola, P. and Jones, M. (2001). Rapid object detection using a boosted cascade of simple features. In Computer Vision and Pattern Recognition, 2001. CVPR 2001. Proceedings of the 2001 IEEE Computer Society Conference on, volume 1, pages I-511 - I-518 vol.1.
  18. Zhang, L., Chu, R., Xiang, S., Liao, S., and Li, S. Z. (2007). Face detection based on multi-block lbp representation. In Proceedings of the 2007 international conference on Advances in Biometrics, ICB'07, pages 11- 18, Berlin, Heidelberg. Springer-Verlag.
Download


Paper Citation


in Harvard Style

Fusek R., Sojka E., Mozdřeň K. and Šurkala M. (2014). Hierarchical Energy-transfer Features . In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 695-702. DOI: 10.5220/0004829506950702


in Bibtex Style

@conference{icpram14,
author={Radovan Fusek and Eduard Sojka and Karel Mozdřeň and Milan Šurkala},
title={Hierarchical Energy-transfer Features},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2014},
pages={695-702},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004829506950702},
isbn={978-989-758-018-5},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Hierarchical Energy-transfer Features
SN - 978-989-758-018-5
AU - Fusek R.
AU - Sojka E.
AU - Mozdřeň K.
AU - Šurkala M.
PY - 2014
SP - 695
EP - 702
DO - 10.5220/0004829506950702