Initialization Framework for Latent Variable Models

Heydar Maboudi Afkham, Carl Henrik Ek, Stefan Carlsson

Abstract

In this paper, we discuss the properties of a class of latent variable models that assumes each labeled sample is associated with set of different features, with no prior knowledge of which feature is the most relevant feature to be used. Deformable-Part Models (DPM) can be seen as good example of such models. While Latent SVM framework (LSVM) has proven to be an efficient tool for solving these models, we will argue that the solution found by this tool is very sensitive to the initialization. To decrease this dependency, we propose a novel clustering procedure, for these problems, to find cluster centers that are shared by several sample sets while ignoring the rest of the cluster centers. As we will show, these cluster centers will provide a robust initialization for the LSVM framework.

References

  1. Azizpour, H. and Laptev, I. (2012). Object Detection Using Strongly-Supervised Deformable Part Models. In ECCV, pages 836-849.
  2. Dalal, N. and Triggs, B. (2005). Histograms of Oriented Gradients for Human Detection. In CVPR (1), pages 886-893.
  3. Felzenszwalb, P. F., Girshick, R. B., McAllester, D., and Ramanan, D. (2010). Object Detection with Discriminatively Trained Part-Based Models. PAMI, 32(9):1627-1645.
  4. Felzenszwalb, P. F. and Huttenlocher, D. P. (2005). Pictorial Structures for Object Recognition. IJCV, 61(1):55- 79.
  5. Heitz, G., Elidan, G., Packer, B., and Koller, D. (2009). Shape-Based Object Localization for Descriptive Classification. International Journal of Computer Vision, 84(1):40-62.
  6. Kumar, M. P., Packer, B., and Koller, D. (2010). Self-Paced Learning for Latent Variable Models. In Lafferty, J., Williams, C. K. I., Shawe-Taylor, J., Zemel, R. S., and Culotta, A., editors, Advances in Neural Information Processing Systems 23, pages 1189-1197.
  7. Lowe, D. G. (2004). Distinctive image features from scaleinvariant keypoints. International Journal of Computer Vision, 60(2):91-110.
  8. Vedaldi, A. and Fulkerson, B. (2008). VLFeat: An Open and Portable Library of Computer Vision Algorithms. Technical report.
  9. Yang, W., Wang, Y., Vahdat, A., and Mori, G. (2012). Kernel Latent SVM for Visual Recognition. In Bartlett, P., Pereira, F. C. N., Burges, C. J. C., Bottou, L., and Weinberger, K. Q., editors, Advances in Neural Information Processing Systems 25, pages 818-826.
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Paper Citation


in Harvard Style

Maboudi Afkham H., Ek C. and Carlsson S. (2014). Initialization Framework for Latent Variable Models . In Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-018-5, pages 227-232. DOI: 10.5220/0004826302270232


in Bibtex Style

@conference{icpram14,
author={Heydar Maboudi Afkham and Carl Henrik Ek and Stefan Carlsson},
title={Initialization Framework for Latent Variable Models},
booktitle={Proceedings of the 3rd International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2014},
pages={227-232},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0004826302270232},
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 - Initialization Framework for Latent Variable Models
SN - 978-989-758-018-5
AU - Maboudi Afkham H.
AU - Ek C.
AU - Carlsson S.
PY - 2014
SP - 227
EP - 232
DO - 10.5220/0004826302270232