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.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsPreview
Unable to display preview. Download preview PDF.
References
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)
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)
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)
Phillips, D.: Image Processing in C, 2nd edn. R and D publication (2000)
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)
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)
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)
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)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights 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)