Compression Techniques for Deep Fisher Vectors

Sarah Ahmed, Tayyaba Azim

2017

Abstract

This paper investigates the use of efficient compression techniques for Fisher vectors derived from deep architectures such as Restricted Boltzmann machine (RBM). Fisher representations have recently created a surge of interest by proving their worth for large scale object recognition and retrieval problems, however they suffer from the problem of large dimensionality as well as have some intrinsic properties that make them unique from the conventional bag of visual words (BoW) features. We have shown empirically which of the normalisation and state of the art compression techniques is well suited for deep Fisher vectors making them amenable for large scale visual retrieval with reduced memory footprint. We further show that the compressed Fisher vectors give impressive classification results even with costless linear classifiers like k-nearest neighbour.

References

  1. Azim, T. and Niranjan, M. (2013). Inducing Discrimination in Biologically Inspired Models of Visual Scene Recognition. In IEEE International Workshop on Machine Learning for Signal Processing (MLSP).
  2. Boureau, Y., Bach, F., LeCun, Y., and Ponce, J. (2010). Learning Mid-level Features for Recognition. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition.
  3. Chatfield, K., Lempitsky, V., Vedaldi, A., and Zisserman, A. (2011). The Devil is in the Details: An Evaluation of Recent Feature Encoding Methods. In Proceedings of the British Machine Vision Conference. BMVA Press.
  4. Csurka, G., Dance, C., Fan, L., Willamowski, J., and Bray, C. (2004). Visual Categorization with Bags of Keypoints. In Workshop on Statistical Learning in Computer Vision, ECCV. Prague.
  5. Everingham, M., Eslami, S., Van Gool, L., Williams, C., Winn, J., and Zisserman, A. (2015). The pascal visual object classes challenge: A retrospective. International Journal of Computer Vision, 111(1):98-136.
  6. Farquhar, J., Szedmak, S., Meng, H., and Shawe-Taylor, J. (2005). Improving Bag-of-Keypoints Image Categorisation: Generative Models and PDF-Kernels.
  7. Hinton, G. (2002). Training products of experts by minimizing contrastive divergence. Neural Computation, 14:1771-1800.
  8. Hinton, G. (2010). A Practical Guide to Training Restricted Boltzmann Machines. Technical report.
  9. Hinton, G. and Salakhutdinov, R. (2006). Reducing the Dimensionality of Data with Neural Networks. Science.
  10. Jaakkola, T. and Haussler, D. (1998). Exploiting Generative Models in Discriminative Classifiers. In Advances in Neural Information Processing Systems 11, pages 487-493. MIT Press.
  11. Jayalakshmi, T. and Santhakumaran, A. (2011). Statistical Normalization and Back Propagation for Classification. International Journal of Computer Theory and Engineering.
  12. Lazebnik, S., Schmid, C., and Ponce, J. (2006). Beyond Bags of Features: Spatial Pyramid Matching for Recognizing Natural Scene Categories. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'06). IEEE.
  13. Maaten, L. (2009). Learning a Parametric Embedding by Preserving Local Structure. RBM.
  14. Maaten, L. (2011). Learning Discriminative Fisher Kernels. In Getoor, L. and Scheffer, T., editors, Proceedings of the 28th International Conference on Machine Learning, ICML 2011, Bellevue, Washington, USA, June 28 - July 2, 2011, pages 217-224. Omnipress.
  15. Pearson, K. (1901). On lines and planes of closest fit to systems of points in space. Philosophical Magazine, 2:559-572.
  16. Perronnin, F. and Dance, C. (2007). Fisher Kernels on Visual Vocabularies for Image Categorization. In IEEE Conference on Computer Vision and Pattern Recognition. IEEE.
  17. Perronnin, F., Dance, C., Csurka, G., and Bressan, M. (2006). Adapted Vocabularies for Generic Visual Categorization. Springer Berlin Heidelberg.
  18. Perronnin, F. and Larlus, D. (2015). Fisher Vectors Meet Neural Networks: A Hybrid Classification Architecture. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. CVPR.
  19. Perronnin, F., Sánchez, J., and Mensink, T. (2010). Improving the Fisher Kernel for Large-scale Image Classification. In European Conference on Computer Vision. Springer.
  20. Russakovsky, O., Deng, J., Su, H., Krause, J., Satheesh, S., Sean, M., Zhiheng, H., Karpathy, A., Khosla, A., Bernstein, M., Berg, A., and Fei-Fei, L. (2015). ImageNet Large Scale Visual Recognition Challenge. International Journal of Computer Vision (IJCV), 115(3):211-252.
  21. Sanchez, J., Perronnin, F., Mensink, T., and Verbeek, J. (2013). Compressed Fisher Vectors for Large-scale Image Classification. IJCV.
  22. Wang, J., Yang, J., Yu, K., Lv, F., Huang, T., and Gong, Y. (2010). Locality-constrained Linear Coding for Image Classification. In IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE.
  23. Weiss, Y., Torralba, A., and Fergus, R. (2009). Spectral Hashing. In Advances in Neural Information Processing Systems. NIPS.
  24. Zhang, Y., Wu, J., and Cai, J. (2014). Compact Representation for Image Classification: To Choose or to Compress? In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. IEEE Computer Society.
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Paper Citation


in Harvard Style

Ahmed S. and Azim T. (2017). Compression Techniques for Deep Fisher Vectors . In Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM, ISBN 978-989-758-222-6, pages 217-224. DOI: 10.5220/0006205002170224


in Bibtex Style

@conference{icpram17,
author={Sarah Ahmed and Tayyaba Azim},
title={Compression Techniques for Deep Fisher Vectors},
booktitle={Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,},
year={2017},
pages={217-224},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006205002170224},
isbn={978-989-758-222-6},
}


in EndNote Style

TY - CONF
JO - Proceedings of the 6th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Compression Techniques for Deep Fisher Vectors
SN - 978-989-758-222-6
AU - Ahmed S.
AU - Azim T.
PY - 2017
SP - 217
EP - 224
DO - 10.5220/0006205002170224