Skip to main content
SpringerLink
Log in

Clustering and Classification of Time Series Representing Sign Language Words

  • Conference paper
Artificial Intelligence and Soft Computing (ICAISC 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 7895))

Included in the following conference series:

Abstract

The paper considers time series with known class labels representing 101 words of Polish sign language (PSL) performed many times in front of a camera. Three clustering algorithms: K–means, K–medoids and Minimum Entropy Clustering (MEC) are compared. Preliminary partitioning of the data set is performed with help of immune based optimisation. Some time series representations and different clustering quality indices are considered. It is shown that clustering is able to reveal existing natural division. Moreover, it gives an opportunity to learn the issues of processing large number of multidimensional data and to identify potential problems which may occur in automatic classification of signed expressions. Results of ten–fold cross–validation tests for nearest neighbour classification are also given.

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

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 39.99
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 54.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. Achtert, E., Bernecker, T., Kriegel, H.-P., Schubert, E., Zimek, A.: Elki in time: Elki 0.2 for the performance evaluation of distance measures for time series. In: Mamoulis, N., Seidl, T., Pedersen, T.B., Torp, K., Assent, I. (eds.) SSTD 2009. LNCS, vol. 5644, pp. 436–440. Springer, Heidelberg (2009)

    Chapter  Google Scholar 

  2. Ankerst, M., Breunig, M.M., Kriegel, H.-P., Sander, J.: Optics: ordering points to identify the clustering structure. SIGMOD Record 28(2), 49–60 (1999)

    Article  Google Scholar 

  3. Athitsos, V., Neidle, C., Sclaroff, S., Nash, J., Stefan, R., Thangali, A., Wang, H., Yuan, Q.: Large lexicon project: American sign language video corpus and sign language indexing/retrieval algorithms (2010)

    Google Scholar 

  4. Awad, G., Han, J., Sutherland, A.: Novel boosting framework for subunit–based sign language recognition. In: Proceedings of the 16th IEEE International Conference on Image Processing, ICIP 2009, pp. 2693–2696. IEEE Press, Piscataway (2009)

    Google Scholar 

  5. Chen, Y., Garcia, E.K., Gupta, M.R., Rahimi, A., Cazzanti, L.: Similarity–based Classification: Concepts and Algorithms. Journal of Machine Learning Research, 747–776 (2009)

    Google Scholar 

  6. Cooper, H.: Sign Language Recognition: Generalising to More Complex Corpora. Centre For Vision Speech and Signal Processing. PhD thesis, University Of Surrey (2010)

    Google Scholar 

  7. Davidson, I., Satyanarayana, A.: Speeding up k-means Clustering by Bootstrap Averaging. In: Proceedings of the IEEE ICDM Workshop on Clustering Large Data Sets, pp. 16–25 (2003)

    Google Scholar 

  8. de Castro, L.N., Von Zuben, F.J.: Learning and optimization using the clonal selection principle. IEEE Transactions on Evolutionary Computation 6(3), 239–251 (2002)

    Article  Google Scholar 

  9. Elkan, C.: Using the Triangle Inequality to Accelerate k-Means. In: ICML, pp. 147–153. AAAI Press (2003)

    Google Scholar 

  10. Flasinski, M., Myslinski, S.: On the use of graph parsing for recognition of isolated hand postures of Polish sign language. Pattern Recognition 43(6), 2249–2264 (2010)

    Article  Google Scholar 

  11. Frahling, G., Sohler, C.: A fast k–means implementation using coresets. In: Symposium on Computational Geometry, pp. 135–143 (2006)

    Google Scholar 

  12. Fu, T.-C.: A review on time series data mining. Engineering Applications of Artificial Intelligence 24(1), 164–181 (2011)

    Article  Google Scholar 

  13. Hein, A., Kirste, T.: Unsupervised detection of motion primitives in very high dimensional sensor data. In: Gottfried, B., Aghajan, H.K. (eds.) Proceedings of the 5th Workshop on Behaviour Monitoring and Interpretation, BMI 2010, Karlsruhe, Germany. CEUR Workshop Proceedings (2010)

    Google Scholar 

  14. Hendzel, J.K.: Polish Sign Language Dictionary. Publishing House Pojezierze (1986) (in polish)

    Google Scholar 

  15. Keogh, E., Lin, J.: Hot sax: Efficiently finding the most unusual time series subsequence. In: Fifth IEEE International Conference on Data Mining, pp. 226–233 (2005)

    Google Scholar 

  16. Kosmidou, V., Petrantonakis, P., Hadjileontiadis, L.J.: Enhanced sign language recognition using weighted intrinsic-mode entropy and signer’s level of deafness. IEEE Transactions on Systems, Man, and Cybernetics, Part B 41(6), 1531–1543 (2011)

    Article  Google Scholar 

  17. Kriegel, H.-P., Kröger, P., Zimek, A.: Clustering high-dimensional data: A survey on subspace clustering, pattern-based clustering, and correlation clustering. ACM The Transactions on Knowledge Discovery from Data 3(1), 1–58 (2009)

    Article  Google Scholar 

  18. Li, H., Zhang, K., Jiang, T.: Minimum entropy clustering and applications to gene expression analysis. In: Proceedings of the 2004 IEEE Computational Systems Bioinformatics Conference, CSB 2004, Washington, DC, pp. 142–151 (2004)

    Google Scholar 

  19. Liao, W.T.: Clustering of time series data a survey. Pattern Recognition 38(11), 1857–1874 (2005)

    Article  MATH  Google Scholar 

  20. Lin, D.J., Le, V., Huang, T.S.: Human–computer interaction. Visual Analysis of Humans, pp. 493–510 (2011)

    Google Scholar 

  21. Maulik, U., Bandyopadhyay, S.: Performance evaluation of some clustering algorithms and validity indices. IEEE Transactions on Pattern Analysis and Machine Intelligence 24(12), 1650–1654 (2002)

    Article  Google Scholar 

  22. MEC: Minimum entropy clustering Java package, http://www.cs.ucr.edu/~hli/mec/ (accessed August 10, 2012)

  23. Ong, S.C.W., Ranganath, S.: Automatic sign language analysis: A survey and the future beyond lexical meaning. IEEE Transactions on Pattern Analysis and Machine Intelligence 27(6), 873–891 (2005)

    Article  Google Scholar 

  24. Oszust, M., Wysocki, M.: Modelling and recognition of signed expressions using subunits obtained by data–driven approach. In: Ramsay, A., Agre, G. (eds.) AIMSA 2012. LNCS, vol. 7557, pp. 315–324. Springer, Heidelberg (2012)

    Chapter  Google Scholar 

  25. Szczepankowski, B.: Sign language at School. WSiP (1988) (in polish)

    Google Scholar 

  26. Theodoridis, S., Koutroumbas, K.: Pattern Recognition, 4th edn. Academic Press (2008)

    Google Scholar 

  27. Tomaszewski, P.: Visual phonology of Polish Sign Language. Publishing House Matrix (2010) (in polish)

    Google Scholar 

  28. Trojanowski, K., Wierzchon, S.: Immune-based algorithms for dynamic optimization. Information Sciences 179(10), 1495–1515 (2009)

    Article  Google Scholar 

  29. Tseng, V.S., Chen, C.-H., Huang, P.-C., Hong, T.-P.: Cluster-based genetic segmentation of time series with DWT. Pattern Recognition Letters 30(13), 1190–1197 (2009)

    Article  Google Scholar 

  30. Vogler, C., Metaxas, D.N.: Toward scalability in ASL recognition: Breaking down signs into phonemes. In: Braffort, A., Gibet, S., Teil, D., Gherbi, R., Richardson, J. (eds.) GW 1999. LNCS (LNAI), vol. 1739, pp. 211–224. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  31. Wang, Q., Chen, X., Zhang, L.-G., Wang, C., Gao, W.: Viewpoint invariant sign language recognition. Computer Vision and Image Understanding 108(1-2), 87–97 (2007)

    Article  Google Scholar 

  32. Warmuth, M.K., Kuzmin, D.: Randomized online PCA algorithms with regret bounds that are logarithmic in the dimension. Journal of Machine Learning Research 9, 2287–2320 (2008)

    MathSciNet  MATH  Google Scholar 

  33. Wysocki, M., Kapuscinski, T., Marnik, J., Oszust, M.: Vision–based Hand Gesture Recognition. Rzeszow University of Technology Publishing House (2011) (in polish)

    Google Scholar 

  34. Xu, R., Wunsch, D.: Clustering. Wiley-IEEE Press (2009)

    Google Scholar 

  35. Zahedi, M., Manashty, A.R.: Robust sign language recognition system using ToF depth cameras. CoRR (2011)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer and Control Engineering, Rzeszow University of Technology, 2 Wincentego Pola, 35-959, Rzeszow, Poland

    Mariusz Oszust & Marian Wysocki

Authors
  1. Mariusz Oszust
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Marian Wysocki
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Częstochowa University of Technology, Armii Krajowej 36, 42-200, Częstochowa, Poland

    Leszek Rutkowski , Marcin Korytkowski  & Rafał Scherer ,  & 

  2. AGH University of Science and Technology, Michiewicza 30, 30-059, Kraków, Poland

    Ryszard Tadeusiewicz

  3. Department of Electrical Engineering and Computer Sciences, University of California, 94720-1776, Berkeley, CA, USA

    Lotfi A. Zadeh

  4. Electrical and Computer Engineering, University of Louisville, 405 Lutz Hall, 40292, Louisville, KY, USA

    Jacek M. Zurada

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Oszust, M., Wysocki, M. (2013). Clustering and Classification of Time Series Representing Sign Language Words. In: Rutkowski, L., Korytkowski, M., Scherer, R., Tadeusiewicz, R., Zadeh, L.A., Zurada, J.M. (eds) Artificial Intelligence and Soft Computing. ICAISC 2013. Lecture Notes in Computer Science(), vol 7895. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38610-7_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-38610-7_21

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38609-1

  • Online ISBN: 978-3-642-38610-7

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Search

Navigation