[go: up one dir, main page]
More Web Proxy on the site http://driver.im/
Skip to main content
Springer Nature Link
Log in

A Modified Isomap Approach to Manifold Learning in Word Spotting

  • Conference paper
  • First Online:
Pattern Recognition (DAGM 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9358))

Included in the following conference series:

Abstract

Word spotting is an effective paradigm for indexing document images with minimal human effort. Here, the use of the Bag-of-Features principle has been shown to achieve competitive results on different benchmarks. Recently, a spatial pyramid approach was used as a word image representation to improve the retrieval results even further. The high dimensionality of the spatial pyramids was attempted to be countered by applying Latent Semantic Analysis. However, this leads to increasingly worse results when reducing to lower dimensions. In this paper, we propose a new approach to reducing the dimensionality of word image descriptors which is based on a modified version of the Isomap Manifold Learning algorithm. This approach is able to not only outperform Latent Semantic Analysis but also to reduce a word image descriptor to up to \(0.12\,\%\) of its original size without losing retrieval precision. We evaluate our approach on two different datasets.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution

Institutional subscriptions

Similar content being viewed by others

Notes

  1. 1.

    http://transcriptorium.eu/~icdar15kws/data.html.

References

  1. Ahonen, T., Hadid, A., Pietik, M., Pietikäinen, M.: Face recognition with local binary patterns. In: European Conference on Computer Vision, pp. 469–481 (2004)

    Google Scholar 

  2. Aldavert, D., Rusinol, M., Toledo, R., Llados, J.: Integrating visual and textual cues for query-by-string word spotting. In: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, pp. 511–515 (2013)

    Google Scholar 

  3. Almazan, J., Fornes, A., Valveny, E.: Deformable HOG-based shape descriptor. In: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, pp. 1022–1026 (2013)

    Google Scholar 

  4. Almazan, J., Gordo, A., Fornes, A., Valveny, E.: Word spotting and recognition with embedded attributes. IEEE Trans. Pattern Anal. Mach. Intell. 36(12), 2552–2566 (2014)

    Article  Google Scholar 

  5. Bengio, Y., Paiement, J.F., Vincent, P., Delalllaux, O., Le Roux, N., Ouimet, M.: Out-of-sample extensions for LLE, Isomap, MDS, eigenmaps and spectral clustering. In: Advances in Neural Information Processing Systems, vol. 16, pp. 177–184 (2004)

    Google Scholar 

  6. Perronnin, F., Sánchez, J., Mensink, T.: Improving the Fisher kernel for large-scale image classification. In: Daniilidis, K., Maragos, P., Paragios, N. (eds.) ECCV 2010, Part IV. LNCS, vol. 6314, pp. 143–156. Springer, Heidelberg (2010)

    Chapter  Google Scholar 

  7. Rath, T.M., Manmatha, R.: Word spotting for historical documents. Int. J. Doc. Anal. Recogn. 9, 139–152 (2007)

    Article  Google Scholar 

  8. Rothacker, L., Rusinol, M., Fink, G.A.: Bag-of-features HMMs for segmentation-free word spotting in handwritten documents. In: Proceedings of the International Conference on Document Analysis and Recognition, ICDAR, pp. 1305–1309 (2013)

    Google Scholar 

  9. Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290, 2323–2326 (2000)

    Article  Google Scholar 

  10. Rusiñol, M., Aldavert, D., Toledo, R., Lladós, J.: Efficient segmentation-free keyword spotting in historical document collections. Pattern Recognit. 48(2), 545–555 (2015)

    Article  Google Scholar 

  11. Silva, V.D., Tenenbaum, J.B.: Global versus local methods in nonlinear dimensionality reduction. In: Advances in Neural Information Processing Systems, vol. 15, pp. 705–712 (2003)

    Google Scholar 

  12. Tenenbaum, J.B., de Silva, V., Langford, J.C.: A global geometric framework for nonlinear dimensionality reduction. Science 290(5500), 2319–2323 (2000)

    Article  Google Scholar 

  13. Zhang, Z.Y., Zha, H.Y.: Principal manifolds and nonlinear dimensionality reduction via tangent space alignment. SIAM J. Sci. Comput. 26(1), 313–338 (2005)

    Article  MathSciNet  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Technische Universität Dortmund, Dortmund, Germany

    Sebastian Sudholt & Gernot A. Fink

Authors
  1. Sebastian Sudholt
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Gernot A. Fink
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Sebastian Sudholt .

Editor information

Editors and Affiliations

  1. Institute of Computer Science III, University of Bonn, Bonn, Germany

    Juergen Gall

  2. MPI for Intelligent Systems, University of Tübingen, Tübingen, Germany

    Peter Gehler

  3. Computer Vision Group, Visual Computing Institute, RWTH Aachen, Aachen, Germany

    Bastian Leibe

Rights and permissions

Reprints and permissions

Copyright information

© 2015 Springer International Publishing Switzerland

About this paper

Cite this paper

Sudholt, S., Fink, G.A. (2015). A Modified Isomap Approach to Manifold Learning in Word Spotting. In: Gall, J., Gehler, P., Leibe, B. (eds) Pattern Recognition. DAGM 2015. Lecture Notes in Computer Science(), vol 9358. Springer, Cham. https://doi.org/10.1007/978-3-319-24947-6_44

Download citation

  • DOI: https://doi.org/10.1007/978-3-319-24947-6_44

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-24946-9

  • Online ISBN: 978-3-319-24947-6

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation