Skip to main content

Time Series Quanslet: A Novel Primitive for Image Classification

  • Conference paper
Contemporary Computing (IC3 2012)

Part of the book series: Communications in Computer and Information Science ((CCIS,volume 306))

Included in the following conference series:

Abstract

successful indexing/categorization of images greatly enhance the performance of content based retrieval systems by filtering out irrelevant classes. This rather difficult problem has not been adequately addressed in current image database systems. In this paper we have introduced a novel feature for classification of image data by taking the one dimensional representation of it (time series) as our input data. Here we have chosen local shape feature instead of global shape feature for the said purpose which enhances its consistency in case of distorted and mutilated shapes.

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

Access this chapter

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. Keogh, E., Wei, L., Xi, X., Lee, S.-H., Vlachos, M.: Lb_keogh supports exact indexing of shapes under rotation invariance with arbitrary representations and distance measures. In: Proceedings of the 32nd International Conference on Very Large Data Bases. VLDB Endowment, pp. 882–893 (2006)

    Google Scholar 

  2. Ding, H., Trajcevski, G., Scheuermann, P., Wang, X., Keogh, E.: Querying and mining of time series data: experimental comparison of representations and distance measures. In: Proc. VLDB Endow., vol. 1(2), pp. 1542–1552 (2008)

    Google Scholar 

  3. Salzberg, S.L.: On comparing classifiers: Pitfalls to avoid and a recommended approach. In: Data Mining and Knowledge Discovery, vol. 1(3), pp. 317–328 (1997)

    Google Scholar 

  4. Phillips, D.: Image Processing in C, 2nd edn. R and D publication (2000)

    Google Scholar 

  5. Geurts, P.: Pattern Extraction for Time Series Classification. In: Siebes, A., De Raedt, L. (eds.) PKDD 2001. LNCS (LNAI), vol. 2168, pp. 115–127. Springer, Heidelberg (2001)

    Chapter  Google Scholar 

  6. Ye, L., Keogh, E.: Time series shapelets: a new primitive for data mining. In: Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 947–956. ACM (2009)

    Google Scholar 

  7. Chiu, B., Keogh, E., Lonardi, S.: Probabilistic discovery of time series motifs. In: Proceedings of the Ninth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, ser. KDD 2003, pp. 493–498. ACM (2003)

    Google Scholar 

  8. Cohen, P., Heeringa, B., Adams, N.: Unsupervised segmentation of categorical time series into episodes. In: Proceedings of the 2002 IEEE International Conference on Data Mining, pp. 99–106. IEEE Computer Society (2002)

    Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2012 Springer-Verlag Berlin Heidelberg

About this paper

Cite this paper

Mishra, T.K., Pujari, A.K. (2012). Time Series Quanslet: A Novel Primitive for Image Classification. In: Parashar, M., Kaushik, D., Rana, O.F., Samtaney, R., Yang, Y., Zomaya, A. (eds) Contemporary Computing. IC3 2012. Communications in Computer and Information Science, vol 306. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-32129-0_12

Download citation

  • DOI: https://doi.org/10.1007/978-3-642-32129-0_12

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-32128-3

  • Online ISBN: 978-3-642-32129-0

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics