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Manifold Learning for Hand Pose Recognition: Evaluation Framework

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Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery (BDAS 2015, BDAS 2016)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 613))

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Abstract

Hand pose recognition from 2D still images is an important, yet very challenging problem of data analysis and pattern recognition. Among many approaches proposed, there have been some attempts to exploit manifold learning for recovering intrinsic hand pose features from the hand appearance. Although they were reported successful in solving particular problems related with recognizing a hand pose, there is a lack of a thorough study on how well these methods discover the intrinsic hand dimensionality. In this study, we introduce an evaluation framework to assess several state-of-the-art methods for manifold learning and we report the results obtained for a set of artificial images generated from a hand model. This will help in future deployments of manifold learning to hand pose estimation, but also to other multidimensional problems common to the big data scenarios.

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Notes

  1. 1.

    HGR dataset is available at http://sun.aei.polsl.pl/~mkawulok/gestures.

  2. 2.

    Available at http://lvdmaaten.github.io/drtoolbox.

References

  1. Belongie, S., Malik, J., Puzicha, J.: Shape matching and object recognition using shape contexts. IEEE Trans. Pattern Anal. Mach. Intell. 24(4), 509–522 (2002)

    Article  Google Scholar 

  2. Donoho, D.L., Grimes, C.: Hessian eigenmaps: locally linear embedding techniques for high-dimensional data. Proc. Nat. Acad. Sci. 100(10), 5591–5596 (2003)

    Article  MathSciNet  MATH  Google Scholar 

  3. Freeman, W.T., Roth, M.: Orientation histograms for hand gesture recognition. In: Proceedings of the IEEE Conference on Automatic Face and Gesture Recognition, FG, pp. 296–301 (1995)

    Google Scholar 

  4. Ge, S.S., Yang, Y., Lee, T.H.: Hand gesture recognition and tracking based on distributed locally linear embedding. Image Vis. Comput. 26(12), 1607–1620 (2008)

    Article  Google Scholar 

  5. Grzejszczak, T., Gałuszka, A., Niezabitowski, M., Radlak, K.: Comparison of hand feature points detection methods. In: Camarinha-Matos, L.M., Barrento, N.S., Mendonça, R. (eds.) DoCEIS 2014. IFIP AICT, vol. 423, pp. 167–174. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  6. Hachaj, T., Ogiela, M.R.: Human actions recognition on multimedia hardware using angle-based and coordinate-based features and multivariate continuous hidden markov model classifier. Multimedia Tools and Applications, pp. 1–21 (in press)

    Google Scholar 

  7. Huttenlocher, D., Klanderman, G., Rucklidge, W.: Comparing images using the hausdorff distance. IEEE TPAMI 15(9), 850–863 (1993)

    Article  Google Scholar 

  8. Kasprowski, P.: Mining of eye movement data to discover people intentions. In: Kozielski, S., Mrozek, D., Kasprowski, P., Malysiak-Mrozek, B., Kostrzewa, D. (eds.) BDAS 2014. CCIS, vol. 424, pp. 355–363. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  9. Kawulok, M., Kawulok, J., Nalepa, J., Papiez, M.: Skin detection using spatial analysis with adaptive seed. In: Proceedings of the IEEE International Conference on Image Processing, ICIP 2013, pp. 3720–3724, September 2013

    Google Scholar 

  10. Kawulok, M., Kawulok, J., Nalepa, J., Smolka, B.: Self-adaptive algorithm for segmenting skin regions. EURASIP J. Adv. Sig. Process. 2014(170), 1–22 (2014)

    Google Scholar 

  11. Kawulok, M., Smolka, B.: Competitive image colorization. In: Proceedings of the IEEE International Conference on Image Processing, ICIP 2010, pp. 405–408 (2010)

    Google Scholar 

  12. Kawulok, M., Wu, J., Hancock, E.R.: Supervised relevance maps for increasing the distinctiveness of facial images. Pattern Recogn. 44(4), 929–939 (2011)

    Article  Google Scholar 

  13. Lee, J.A., Verleysen, M.: Nonlinear dimensionality reduction of data manifolds with essential loops. Neurocomputing 67, 29–53 (2005)

    Article  Google Scholar 

  14. van der Maaten, L., Hinton, G.: Visualizing data using t-SNE. J. Mach. Learn. Res. 9(2579–2605), 85 (2008)

    MATH  Google Scholar 

  15. van der Maaten, L., Postma, E.O., Herik, H.J.: Dimensionality reduction: a comparative review. J. Mach. Learn. Res. 10(1–41), 66–71 (2009)

    Google Scholar 

  16. Milligan, G.W., Cooper, M.C.: A study of the comparability of external criteria for hierarchical cluster analysis. Multivar. Behav. Res. 21(4), 441–458 (1986)

    Article  Google Scholar 

  17. Nalepa, J., Grzejszczak, T., Kawulok, M.: Wrist localization in color images for hand gesture recognition. In: Gruca, A., Czachórski, T., Kozielski, S. (eds.) Man-Machine Interactions 3. AISC, vol. 242, pp. 81–90. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  18. Nalepa, J., Kawulok, M.: Fast and accurate hand shape classification. In: Kozielski, S., Mrozek, D., Kasprowski, P., Malysiak-Mrozek, B., Kostrzewa, D. (eds.) BDAS 2014. CCIS, vol. 424, pp. 364–373. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

  19. Nalepa, J., Kawulok, M.: Adaptive memetic algorithm enhanced with data geometry analysis to select training data for SVMs. Neurocomputing 185, 113–132 (2016). http://dx.doi.org/10.1016/j.neucom.2015.12.046

    Article  Google Scholar 

  20. Nurzynska, K., Smolka, B.: PCA application in classification of smiling and neutral facial displays. In: Kozielski, S., Mrozek, D., Kasprowski, P., Malysiak-Mrozek, B., Kostrzewa, D. (eds.) BDAS 2015. CCIS, vol. 521, pp. 398–407. Springer, Heidelberg (2015)

    Google Scholar 

  21. Oikonomidis, I., Kyriazis, N., Argyros, A.A.: Efficient model-based 3D tracking of hand articulations using Kinect. In: BMVC, vol. 1(2), p. 3 (2011)

    Google Scholar 

  22. Roweis, S.T., Saul, L.K.: Nonlinear dimensionality reduction by locally linear embedding. Science 290(5500), 2323–2326 (2000)

    Article  Google Scholar 

  23. Starosolski, R.: New simple and efficient color space transformations for lossless image compression. J. Vis. Commun. Image Represent. 25(5), 1056–1063 (2014)

    Article  Google Scholar 

  24. Szwoch, M.: On facial expressions and emotions RGB-D database. In: Kozielski, S., Mrozek, D., Kasprowski, P., Malysiak-Mrozek, B., Kostrzewa, D. (eds.) BDAS 2014. CCIS, vol. 424, pp. 384–394. Springer, Heidelberg (2014)

    Chapter  Google Scholar 

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

    Article  Google Scholar 

  26. Šarić, M.: Libhand: A library for hand articulation, version 0.9 (2011)

    Google Scholar 

  27. Wang, Y., Luo, Z., Liu, J., Fan, X., Li, H., Wu, Y.: Real-time estimation of hand gestures based on manifold learning from monocular videos. Multimedia Tools Appl. 71(2), 555–574 (2013)

    Article  Google Scholar 

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Authors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Maciej Papiez, Michal Kawulok & Jakub Nalepa

Authors
  1. Maciej Papiez
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  2. Michal Kawulok
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  3. Jakub Nalepa
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Corresponding author

Correspondence to Michal Kawulok .

Editor information

Editors and Affiliations

  1. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Stanisław Kozielski

  2. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Dariusz Mrozek

  3. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Paweł Kasprowski

  4. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Bożena Małysiak-Mrozek

  5. Institute of Informatics, Silesian University of Technology, Gliwice, Poland

    Daniel Kostrzewa

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Papiez, M., Kawulok, M., Nalepa, J. (2016). Manifold Learning for Hand Pose Recognition: Evaluation Framework. In: Kozielski, S., Mrozek, D., Kasprowski, P., Małysiak-Mrozek, B., Kostrzewa, D. (eds) Beyond Databases, Architectures and Structures. Advanced Technologies for Data Mining and Knowledge Discovery. BDAS BDAS 2015 2016. Communications in Computer and Information Science, vol 613. Springer, Cham. https://doi.org/10.1007/978-3-319-34099-9_55

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  • DOI: https://doi.org/10.1007/978-3-319-34099-9_55

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