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Fundamentals of Fisher Kernels

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Composing Fisher Kernels from Deep Neural Models

Part of the book series: SpringerBriefs in Computer Science ((BRIEFSCOMPUTER))

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Abstract

This chapter introduces a specific genre of kernels that draws a formal connection between the generative and discriminative models of learning. Both the paradigms offer unique complementary advantages over one another, yet there always existed a need to combine the best of both the worlds for solving complex problems. This gap was filled by Tommy Jaakola through the introduction of Fisher kernels in 1998 and since then it has played a key role in solving problems from computational biology, computer vision and machine learning. We introduce this concept here and show how to compute Fisher vector encodings from deep models using a toy example in MATLAB.

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Notes

  1. 1.

    We must have twice differentiable likelihood so that the Fisher information I exists and I must be positive definite at the chosen \(\theta \).

References

  1. Jaakkola, T., Haussler, D.: Exploiting generative models in discriminative classifiers. In: Advances in Neural Information Processing Systems, pp. 487–493 (1999)

    Google Scholar 

  2. Nyffenegger, M., Chappelier, J., Gaussier, É.: Revisiting fisher kernels for document similarities. In: European Conference on Machine Learning, pp. 727–734, Springer (2006)

    Google Scholar 

  3. Perronnin, F., Dance, C.: Fisher kernels on visual vocabularies for image categorization. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 1–8, IEEE (2007)

    Google Scholar 

  4. Maaten, L.: Learning discriminative fisher kernels. In: ICML 11, 217–224 (2011)

    Google Scholar 

  5. Perronnin, F., Sánchez, J., Mensink, T.: Improving the fisher kernel for large scale image classification. In: European Conference on Computer Vision, pp. 143–156, Springer (2010)

    Google Scholar 

  6. Ahmed, S., Azim, T.: Compression techniques for deep fisher vectors. In: ICPRAM, pp. 217–224 (2017)

    Google Scholar 

  7. Jaakkola, T., Diekhans, M., Haussler, D.: A discriminative framework for detecting remote protein homologies. J. Comput. Biol. 7(1–2), 95–114 (2000)

    Article  Google Scholar 

  8. Azim, T., Niranjan, M.: Inducing discrimination in biologically inspired models of visual scene recognition. In: IEEE International Workshop on Machine Learning for Signal Processing, (MLSP) (2013)

    Google Scholar 

  9. Smith, N., Niranjan, M.: Data-dependent kernels in SVM classification of speech patterns. Technical Report, Cambridge University (2001)

    Google Scholar 

  10. Moreno, P., Rifkin, R.: Using the fisher kernel method for web audio classification. In: Acoustics, Speech, and Signal Processing (ICASSP), vol. 4, pp. 2417–2420 (2000)

    Google Scholar 

  11. Vinokourov, A., Girolami, M.: Document Classification Employing the Fisher Kernel Derived from Probabilistic Hierarchic Corpus Representations. Springer (2001)

    Google Scholar 

  12. Chappelier, J., Eckard, E.: PLSI: The true fisher kernel and beyond. European Conference on Machine Learning and Knowledge Discovery in Databases: Part I, 195–210 (2009)

    Google Scholar 

  13. Sun, Q., Li, R., Luo, D., et al.: Text segmentation with LDA based fisher kernel. In: Proceedings of the 46th Annual Meeting of the Association for Computational Linguistics on Human Language Technologies: Short Papers, Association for Computational Linguistics, pp. 269–272 (2008)

    Google Scholar 

  14. Elkan, C.: Deriving TF-IDF as a fisher kernel. In: 12th International Conference on String Processing and Information Retrieval (SPIRE), pp. 296–301 (2005)

    Google Scholar 

  15. Azim, T.: Fisher kernels match deep models. Electron. Lett. 53(6), 397–399 (2017)

    Article  Google Scholar 

  16. Azim, T., Niranjan, M.: Texture classification with Fisher kernel extracted from the continuous models of RBM. In: International Conference on Computer Vision Theory and Applications (VISAPP), vol. 2, pp. 684–690, IEEE ( 2014)

    Google Scholar 

  17. Songa, Y., Hong, X., McLoughlin, I.: Image classification with CNN based fisher vector coding. In: Visual Communications and Image Processing (VCIP), pp. 1–4 (2016)

    Google Scholar 

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Authors and Affiliations

  1. Center of Excellence in IT, Institute of Management Sciences, Peshawar, Pakistan

    Tayyaba Azim

  2. Institute of Management Sciences, Peshawar, Pakistan

    Sarah Ahmed

Authors
  1. Tayyaba Azim
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  2. Sarah Ahmed
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Correspondence to Tayyaba Azim .

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Azim, T., Ahmed, S. (2018). Fundamentals of Fisher Kernels. In: Composing Fisher Kernels from Deep Neural Models. SpringerBriefs in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-98524-4_2

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  • DOI: https://doi.org/10.1007/978-3-319-98524-4_2

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-98523-7

  • Online ISBN: 978-3-319-98524-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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