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Change Detection in Multidimensional Data Streams with Efficient Tensor Subspace Model

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Hybrid Artificial Intelligent Systems (HAIS 2018)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 10870))

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Abstract

The paper presents a method for change detection in multidimensional streams of data based on a tensor model constructed from the Higher-Order Singular Value Decomposition of raw data tensors. The method was applied to the problem of video shot detection showing good accuracy and high speed of execution compared with other more time demanding tensor models. In this paper we show two efficient algorithms for tensor model construction and tensor model update from the stream of data.

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References

  1. Asghar, M.N., Hussain, F., Manton, R.: Video indexing: a survey. Int. J. Comput. Inf. Technol. 03(01), 148–169 (2014)

    Google Scholar 

  2. de Avila, S.E.F., Lopes, A.P.B., da Luz Jr., A., Araújo, A.A.: VSUMM: a mechanism designed to produce static video summaries and a novel evaluation method. Pattern Recogn. Lett. 32, 56–68 (2011)

    Article  Google Scholar 

  3. Cayllahua-Cahuina, E.J., Cámara-Chávez, G., Menotti, D.: A Static Video Summarization Approach with Automatic Shot Detection Using Color Histograms (2012)

    Google Scholar 

  4. Cyganek, B., Woźniak, M.: Tensor-based shot boundary detection in video streams. New Gener. Comput. 35(4), 311–340 (2017)

    Article  Google Scholar 

  5. Cyganek, B., Woźniak, M.: On robust computation of tensor classifiers based on the higher-order singular value decomposition. In: Silhavy, R., Senkerik, R., Oplatkova, Z.K., Silhavy, P., Prokopova, Z. (eds.) Software Engineering Perspectives and Application in Intelligent Systems. AISC, vol. 465, pp. 193–201. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-33622-0_18

    Chapter  Google Scholar 

  6. Cyganek, B.: Object Detection and Recognition in Digital Images: Theory and Practice. Wiley, Hoboken (2013)

    Book  Google Scholar 

  7. Cyganek, B.: Recognition of road signs with mixture of neural networks and arbitration modules. In: Wang, J., Yi, Z., Zurada, J.M., Lu, B.-L., Yin, H. (eds.) ISNN 2006. LNCS, vol. 3973, pp. 52–57. Springer, Heidelberg (2006). https://doi.org/10.1007/11760191_8

    Chapter  Google Scholar 

  8. DeRecLib, 2013. http://www.wiley.com/go/cyganekobject

  9. Del Fabro, M., Böszörmenyi, L.: State-of-the-art and future challenges in video scene detection: a survey. Multimedia Syst. 19(5), 427–454 (2013)

    Article  Google Scholar 

  10. Fu, Y., Guo, Y., Zhu, Y., Liu, F., Song, C., Zhou, Z.-H.: Multi-view video summarization. IEEE Trans. Multimedia 12(7), 717–729 (2010)

    Article  Google Scholar 

  11. Furini, M., Geraci, F., Montangero, M., Pellegrini, M.: STIMO: STIll and moving video storyboard for the web scenario. Multimedia Tools Appl. 46(1), 47–69 (2010)

    Article  Google Scholar 

  12. Gao, Y., Wang, W.-B., Yong, J.-H., Gu, H.-J.: Dynamic video summarization using two-level redundancy detection. Multimedia Tools Appl. 42(2), 233–250 (2009)

    Article  Google Scholar 

  13. Guan, G., Wang, Z., Yu, K., Mei, S., He, M., Feng, D.: Video summarization with global and local features. In: Proceedings of the 2012 IEEE International Conference on Multimedia and Expo Workshops, pp. 570–575. IEEE Computer Society, Washington, DC (2012)

    Google Scholar 

  14. https://open-video.org/

  15. https://sites.google.com/site/vsummsite/home

  16. https://sites.google.com/site/vscansite/home

  17. Kolda, T.G., Bader, B.W.: Tensor decompositions and applications. SIAM Rev. 51(3), 455–500 (2008)

    Article  MathSciNet  Google Scholar 

  18. de Lathauwer, L.: Signal Processing Based on Multilinear Algebra. Ph.D. dissertation, Katholieke Universiteit Leuven (1997)

    Google Scholar 

  19. de Lathauwer, L., de Moor, B., Vandewalle, J.: A multilinear singular value decomposition. SIAM J. Matrix Anal. Appl. 21(4), 1253–1278 (2000)

    Article  MathSciNet  Google Scholar 

  20. Lee, H., Yu, J., Im, Y., Gil, J.-M., Park, D.: A unified scheme of shot boundary detection and anchor shot detection in news video story parsing. Multimedia Tools Appl. 51, 1127–1145 (2011)

    Article  Google Scholar 

  21. Mahmoud, K.M., Ismail, M.A., Ghanem, N.M.: VSCAN: an enhanced video summarization using density-based spatial clustering. In: Petrosino, A. (ed.) ICIAP 2013. LNCS, vol. 8156, pp. 733–742. Springer, Heidelberg (2013). https://doi.org/10.1007/978-3-642-41181-6_74

    Chapter  Google Scholar 

  22. Medentzidou, P., Kotropoulos, C.: Video summarization based on shot boundary detection with penalized contrasts. In: IEEE 9th International Symposium on Image and Signal Processing and Analysis (ISPA), pp. 199–203 (2015)

    Google Scholar 

  23. DeMenthon, D., Kobla, V., Doermann, D.: Video summarization by curve simplification. In: Proceedings of the Sixth ACM International Conference on Multimedia, pp. 211–218. ACM (1998)

    Google Scholar 

  24. Mundur, P., Rao, Y., Yesha, Y.: Keyframe-based video summarization using delaunay clustering. Int. J. Dig. Libr. 6(2), 219–232 (2006)

    Article  Google Scholar 

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Acknowledgement

This work was supported by the Polish National Science Center NCN under the grant no. 2014/15/B/ST6/00609 as well as AGH Statutory Funds no. 11.11.230.017.

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Authors and Affiliations

  1. AGH University of Science and Technology, Al. Mickiewicza 30, 30-059, Kraków, Poland

    Bogusław Cyganek

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  1. Bogusław Cyganek
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Correspondence to Bogusław Cyganek .

Editor information

Editors and Affiliations

  1. Department of Mine Operating and Prospection, University of Oviedo, Oviedo, Spain

    Francisco Javier de Cos Juez

  2. Department of Computer Science, University of Oviedo, Oviedo, Spain

    José Ramón Villar

  3. Department of Computer Science, University of Oviedo, Oviedo, Spain

    Enrique A. de la Cal

  4. Department of Civil Engineering, University of Burgos, Burgos, Spain

    Álvaro Herrero

  5. University of A Coruña, A Coruña, Spain

    Héctor Quintián

  6. University of Salamanca, Salamanca, Spain

    José António Sáez

  7. University of Salamanca, Salamanca, Spain

    Emilio Corchado

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Cyganek, B. (2018). Change Detection in Multidimensional Data Streams with Efficient Tensor Subspace Model. In: de Cos Juez, F., et al. Hybrid Artificial Intelligent Systems. HAIS 2018. Lecture Notes in Computer Science(), vol 10870. Springer, Cham. https://doi.org/10.1007/978-3-319-92639-1_58

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  • DOI: https://doi.org/10.1007/978-3-319-92639-1_58

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-92638-4

  • Online ISBN: 978-3-319-92639-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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