International Journal of Intelligent Systems and Applications (IJISA)
IJISA Vol. 9, No. 11, 8 Nov. 2017
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Video Shots’ Matching via Various Length of Multidimensional Time Sequences
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Author(s)
Zhengbing Hu
1,*
Sergii V. Mashtalir
2
Oleksii K. Tyshchenko
2
Mykhailo I. Stolbovyi
2
Index Terms
Time Series Processing, Data Clustering, Video Streams, Visual Attention, Similarity Measures, Dynamic Time Warping
Abstract
Temporal clustering (segmentation) for video streams has revolutionized the world of multimedia. Detected shots are principle units of consecutive sets of images for semantic structuring. Evaluation of time series similarity is based on Dynamic Time Warping and provides various solutions for Content Based Video Information Retrieval. Time series clustering in terms of the iterative Dynamic Time Warping and time series reduction are discussed in the paper.
Cite This Paper
Zhengbing Hu, Sergii V. Mashtalir, Oleksii K. Tyshchenko, Mykhailo I. Stolbovyi, "Video Shots’ Matching via Various Length of Multidimensional Time Sequences", International Journal of Intelligent Systems and Applications(IJISA), Vol.9, No.11, pp.10-16, 2017. DOI:10.5815/ijisa.2017.11.02
Reference
[1]A.K. Jain and R.C. Dubes, Algorithms for Clustering Data. Englewood Cliffs, N.J.: Prentice Hall, 1988.
[2]L. Kaufman and P.J. Rousseeuw, Finding Groups in Data: An Introduction to Cluster Analysis. N.Y.: John Wiley & Sons, Inc., 1990.
[3]J. Han and M. Kamber, Data Mining: Concepts and Techniques. San Francisco: Morgan Kaufmann, 2006.
[4]G. Gan, C. Ma, and J. Wu, Data Clustering: Theory, Algorithms, and Applications. Philadelphia: SIAM, 2007.
[5]J. Abonyi and B. Feil, Cluster Analysis for Data Mining and System Identification. Basel: Birkhäuser, 2007.
[6]D.L. Olson and D. Dursun, Advanced Data Mining Techniques. Berlin: Springer, 2008.
[7]C.C. Aggarwal and C.K. Reddy, Data Clustering: Algorithms and Applications. Boca Raton: CRC Press, 2014.
[8]C.C. Aggarwal, Data Mining. Cham: Springer, Int. Publ. Switzerland, 2015.
[9]Zh. Hu, Ye.V. Bodyanskiy, O.K. Tyshchenko, and V.O. Samitova,"Fuzzy Clustering Data Given in the Ordinal Scale", International Journal of Intelligent Systems and Applications (IJISA), Vol.9, No.1, pp.67-74, 2017.
[10]Zh. Hu, Ye.V. Bodyanskiy, O.K. Tyshchenko, and V.O. Samitova,"Fuzzy Clustering Data Given on the Ordinal Scale Based on Membership and Likelihood Functions Sharing", International Journal of Intelligent Systems and Applications (IJISA), Vol.9, No.2, pp.1-9, 2017.
[11]Zh. Hu, Ye.V. Bodyanskiy, O.K. Tyshchenko, V.O. Samitova,"Possibilistic Fuzzy Clustering for Categorical Data Arrays Based on Frequency Prototypes and Dissimilarity Measures", International Journal of Intelligent Systems and Applications (IJISA), Vol.9, No.5, pp.55-61, 2017.
[12]Zh. Hu, Ye.V. Bodyanskiy, O.K. Tyshchenko, V.M. Tkachov, “Fuzzy Clustering Data Arrays with Omitted Observations”, International Journal of Intelligent Systems and Applications (IJISA), Vol.9, No.6, pp.24-32, 2017.
[13]N. Begum, L. Ulanova, J. Wang, and E. Keogh, “Accelerating dynamic time Warping Clustering with a Novel Admissible Pruning Strategy”, Proc. of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 49-58, 2015.
[14]J. Abonyi, B. Feil, S. Nemett, and P. Arva, “Fuzzy Clustering Based Segmentation of Time-Series”, Advances in Intelligent Data Analysis V (IDA 2003). Lecture Notes in Computer Science, vol. 2810, pp. 275–285, 2003.
[15]8. S. Chu, E. Keogh, D. Hart, and M. Pazzani, “Iterative Deepening Dynamic Time Warping for Time Series”, Proc. 2nd SIAM International Conference on Data Mining (SDM-02), 2002.
[16]L. Zhang, W. Lin, Selective Visual Attention: Computational Models and Applications. Wiley-IEEE Press, 2013.
[17]L. Elazary, L. Itti, “Interesting objects are visually salient”, Journal of Vision, vol. 8(3), pp.1–15, 2008.
[18]O. Le Meur, P. Le Callet, D. Barba, and D. Thoreau, “A coherent computational approach to model the bottom-up visual attention”, IEEE Trans. on PAMI, vol.28, no.5, pp.802-817, 2006.
[19]M.M. Deza and E. Deza, Encyclopedia of Distances. Dordrecht, Heidelberg, London, New York: Springer, 2009.
[20]D.J. Berndt and S. Clifford, “Using Dynamic Time Warping to Find Patterns in Time Series”, Proc. of the 3rd International Conference on Knowledge Discovery and Data Mining (AAAIWS'94), pp.359-370, 1994.
[21]E.J. Keogh and M.J. Pazzani, “Scaling up Dynamic Time Warping for Datamining Applications”, Proc. of the 6th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, pp. 285-289, 2000.
[22]E.J. Keogh and M.J. Pazzani, “Derivative Dynamic Time Warping”, Proc. of the First SIAM International Conference on Data Mining (SDM'2001), 2001.
[23]T. Rakthanmanon, B. Campana, A. Mueen, G. Batista, B. Westover, Q. Zhu, J. Zakaria, and E. Keogh, “Searching and Mining Trillions of Time Series Subsequences under Dynamic Time Warping”, Proc. of the 18th ACM SIGKDD International Conference on Knowledge discovery and data mining (KDD’12), pp. 262-270, 2012.
[24]I.V. Nikiforov, “Sequential Detection of Changes in Stochastic Process”, IFAC Proceedings Volumes, vol.25, iss.15, pp.11-19, 1992.
[25]D. Kinoshenko, V. Mashtalir, and V. Shlyakhov, “A partition metric for clustering features analysis”, International Journal “Information Theories and Applications”, vol.14, iss.3, pp.230-236, 2007.