https://oldena.lpnu.ua/handle/ntb/52443| Title: | Representative Based Clustering of Long Multivariate Sequences with Different Lengths |
| Authors: | Mashtalir, Sergii Mashtalir, Volodymyr Stolbovyi, Mykhailo |
| Affiliation: | Kharkiv National University of Radio Electronics |
| Bibliographic description (Ukraine): | Mashtalir S. Representative Based Clustering of Long Multivariate Sequences with Different Lengths / Sergii Mashtalir, Volodymyr Mashtalir, Mykhailo Stolbovyi // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Львів : Lviv Politechnic Publishing House, 2018. — P. 545–548. — (Machine Vision and Pattern Recognition). |
| Bibliographic description (International): | Mashtalir S. Representative Based Clustering of Long Multivariate Sequences with Different Lengths / Sergii Mashtalir, Volodymyr Mashtalir, Mykhailo Stolbovyi // Data stream mining and processing : proceedings of the IEEE second international conference, 21-25 August 2018, Lviv. — Lviv Politechnic Publishing House, 2018. — P. 545–548. — (Machine Vision and Pattern Recognition). |
| Is part of: | Data stream mining and processing : proceedings of the IEEE second international conference, 2018 |
| Conference/Event: | IEEE second international conference "Data stream mining and processing" |
| Issue Date: | 28-Feb-2018 |
| Publisher: | Lviv Politechnic Publishing House |
| Place of the edition/event: | Львів |
| Temporal Coverage: | 21-25 August 2018, Lviv |
| Keywords: | video stream clustering metric |
| Number of pages: | 4 |
| Page range: | 545-548 |
| Start page: | 545 |
| End page: | 548 |
| Abstract: | Video streams as unstructured or poorly structured data issue a challenge to create a unified framework capable to depict and convey high-level stories. Up-to-date indexing and search techniques to manage video data are able to operate the voluminous amounts of contained in video information in order to detect spatial and temporal events. Nevertheless, bridging semantic gap between the low-level frame or video features and high-level semantic concepts necessitates extremely high-speed procedures of temporal unlabeled data. Automatic video annotation in visual forms appears one of the promising approaches representing most pertinent and crucially important information. This goal is achieved by (among others) clustering large collections of video data. |
| URI: | https://ena.lpnu.ua/handle/ntb/52443 |
| ISBN: | © Національний університет „Львівська політехніка“, 2018 © Національний університет „Львівська політехніка“, 2018 |
| Copyright owner: | © Національний університет “Львівська політехніка”, 2018 |
| References (Ukraine): | [1] C. Liu, Recent Advances in Intelligent Image Search and Video Retrieval. Intelligent Systems Reference Library, vol. 121, Cham: Springer, 2017. [2] G. Csurka, Domain Adaptation in Computer Vision Applications. Advances in Computer Vision and Pattern Recognition, Cham: Springer, 2017. [3] B. T. Truong, and S. Venkatesh, “Video abstraction: A systematic review and classification,” ACM Trans. on Multimedia Computing, Communications, and Applications, vol. 3, iss. 1, pp. 1–37, 2007. [4] S. Mashtalir, and O. Mihnova, “Key frame extraction from video: framework and advances,” Int. J. of Computer Vision and Image Processing, vol. 4, iss. 2, pp. 68-79, 2014. [5] T. Wiatowski, and H. Bölcskei, “A mathematical theory of Deep Convolutional Neural Networks for feature extraction.” IEEE Trans. on Information Theory, vol. 64, iss. 3, pp. 1845–1866, 2018. [6] F. Shih, Image Processing and Pattern Recognition: Fundamentals and Techniques, Hoboken: John Wiley & Sons, Inc., 2010. [7] L. Elazary, and L. Itti, “Interesting objects are visually salient,” J. of Vision, vol. 8, iss.3, pp. 1–15, 2008. [8] D. Liu, G. Hua, and T. Chen “A hierarchical visual model for video object summarization,” IEEE Trans. on PAMI vol. 32, iss. 12, pp. 2178–2190, 2010. [9] Ye. Bodyanskiy, D. Kinoshenko, S. Mashtalir, and O. Mikhnova, “On-line video segmentation using methods of fault detection in multidimensional time sequences,” Int. J. of Electronic Commerce Studies, vol. 3, iss. 1, pp. 1-20, 2012. [10] C.C. Aggarwal, Data Mining. Cham: Springer, 2015. [11] G. Gan, C. Ma, and J. Wu, Data Clustering: Theory, Algorithms, and Applications, ASA-SIAM Series on Statistics and Applied Probability. Philadelphia : SIAM, VA, 2007. [12] Z. Hu, S. V. Mashtalir, O. K. Tyshchenko, and M. I. Stolbovyi “Video shots’ matching via various length of multidimensional time sequences,” Int. J. of Intelligent Systems and Applications (IJISA), vol. 9, iss. 11, pp.10–162, 017. [13] X. Wang, A. Mueen, H. Ding, G. Trajcevski, P. Scheuermann, and E. Keogh, “Experimental comparison of representation methods and distance measures for time series data,” Data Mining and Knowledge Discovery, vol. 26, iss. 2, pp. 275–309, 2012. [14] S. Mashtalir, and V. Mashtalir, “Sequential temporal video segmentation via spatial image partitions,” IEEE First Int. Conf. on Data Stream Mining and Processing (DSMP’2016), Lviv, Ukrane, pp. 239-242, 2016. |
| References (International): | [1] C. Liu, Recent Advances in Intelligent Image Search and Video Retrieval. Intelligent Systems Reference Library, vol. 121, Cham: Springer, 2017. [2] G. Csurka, Domain Adaptation in Computer Vision Applications. Advances in Computer Vision and Pattern Recognition, Cham: Springer, 2017. [3] B. T. Truong, and S. Venkatesh, "Video abstraction: A systematic review and classification," ACM Trans. on Multimedia Computing, Communications, and Applications, vol. 3, iss. 1, pp. 1–37, 2007. [4] S. Mashtalir, and O. Mihnova, "Key frame extraction from video: framework and advances," Int. J. of Computer Vision and Image Processing, vol. 4, iss. 2, pp. 68-79, 2014. [5] T. Wiatowski, and H. Bölcskei, "A mathematical theory of Deep Convolutional Neural Networks for feature extraction." IEEE Trans. on Information Theory, vol. 64, iss. 3, pp. 1845–1866, 2018. [6] F. Shih, Image Processing and Pattern Recognition: Fundamentals and Techniques, Hoboken: John Wiley & Sons, Inc., 2010. [7] L. Elazary, and L. Itti, "Interesting objects are visually salient," J. of Vision, vol. 8, iss.3, pp. 1–15, 2008. [8] D. Liu, G. Hua, and T. Chen "A hierarchical visual model for video object summarization," IEEE Trans. on PAMI vol. 32, iss. 12, pp. 2178–2190, 2010. [9] Ye. Bodyanskiy, D. Kinoshenko, S. Mashtalir, and O. Mikhnova, "On-line video segmentation using methods of fault detection in multidimensional time sequences," Int. J. of Electronic Commerce Studies, vol. 3, iss. 1, pp. 1-20, 2012. [10] C.C. Aggarwal, Data Mining. Cham: Springer, 2015. [11] G. Gan, C. Ma, and J. Wu, Data Clustering: Theory, Algorithms, and Applications, ASA-SIAM Series on Statistics and Applied Probability. Philadelphia : SIAM, VA, 2007. [12] Z. Hu, S. V. Mashtalir, O. K. Tyshchenko, and M. I. Stolbovyi "Video shots’ matching via various length of multidimensional time sequences," Int. J. of Intelligent Systems and Applications (IJISA), vol. 9, iss. 11, pp.10–162, 017. [13] X. Wang, A. Mueen, H. Ding, G. Trajcevski, P. Scheuermann, and E. Keogh, "Experimental comparison of representation methods and distance measures for time series data," Data Mining and Knowledge Discovery, vol. 26, iss. 2, pp. 275–309, 2012. [14] S. Mashtalir, and V. Mashtalir, "Sequential temporal video segmentation via spatial image partitions," IEEE First Int. Conf. on Data Stream Mining and Processing (DSMP’2016), Lviv, Ukrane, pp. 239-242, 2016. |
| Content type: | Conference Abstract |
| Appears in Collections: | Data stream mining and processing : proceedings of the IEEE second international conference |
| File | Description | Size | Format | |
|---|---|---|---|---|
| 2018_Mashtalir_S-Representative_Based_545-548.pdf | 169.07 kB | Adobe PDF | View/Open | |
| 2018_Mashtalir_S-Representative_Based_545-548__COVER.png | 632.88 kB | image/png | View/Open |
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