ADAPTIVE MATRIX MODELS IN THE VIDEO STREAMS CONTROL PROBLEM
DOI:
https://doi.org/10.15588/1607-3274-2018-4-18Keywords:
video data, clustering, adaptive matrix models, tuning criteria, tracking signal.Abstract
Context. At present, the multidimensional data analysis is one of the priority scientific research areas. This is due to the almostuncontrollable growth in the information size and the need to obtain/search for various kinds of useful data from it. At the same time,
video data analysis is one of the most difficult from a computational point of view, not only because of BigData being processed, but
also due to the video unstructuredness, and also the fact that in a bunch of video processing applications exist limitations on the
processing time. One of the ways to solve these video analysis problems is to pre-process the initial data in order to get them split
into homogeneous segments (shots), which significantly reduces the time and computational costs for further content-based video
analysis in video database. And, despite the existing results in this direction, the video sequences clustering/segmentation problem
remains extremely relevant.
Objective. The paper considers the problem of clustering multidimensional streaming data as example of temporal video
segmentation.
Method. A method for controlling changes in streaming data is proposed, which allows you to detect the moments of a
significant change in the input multidimensional data characteristics, based on adaptive matrix models with the specialized tuning
algorithm for the predictive model introduction.
Results. The conducted experiment on an arbitrary video sequences demonstrated the video shot detection possibility. It should
be noted that the proposed approach essentially depends on the input data spatial segmentation results, which is necessary to obtain a
characteristics set describing each frame of the video sequence.
Conclusions. The proposed method allows multidimensional input data clustering/segmentation by adaptive matrix models. As
initial data in the experimental part, video sequences were used.
References
Grundmann M., Kwatra V., Han M., et al. Efficient hierarchical graph-based video segmentation, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2010, pp. 2141–2148. DOI: 10.1109/CVPR.2010.5539893
Galasso F., Cipolla R., Schiele B. Video Segmentation with Superpixels, 11-th Asian Conference on Computer Vision (ACCV), 2012, Volume I, pp. 760–774. DOI:
https://doi.org/10.1007/978-3-642-37331-2_57
Seguin G., Bojanowski P., Lajugie R. et al Instance-level video segmentation from object tracks, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2016, pp. 3678–3687. DOI: 10.1109/CVPR. 2016.400 4. Abonyi J., Feil B., Nemeth S. et al. Fuzzy clustering based segmentation of timeseries, Lecture Notes in Computer Science. Berlin, Springer, 2003, Vol. 2810, pp. 275–285. DOI:https://doi.org/10.1007/978-3-540-45231-7_26
Abonyi J., Feil B., Nemett S. et al. Modified Gath-Geva
clustering for fuzzy segmentation of multivariate timeseries, Fuzzy Sets and Systems, 2005, Vol. 149, Issue 1, pp. 39–56. DOI: 10.1016/j.fss.2004.07.008
Badavas P. C. Real-time statistical process control. Englewood Cliffs, N. J., Prentice Hall, 1993, 232 p.
Bodjans’kyj Je. V., Peleshko D. D., Vynokurova O. A. et al Analiz ta obrobka danyh zasobamy obchisluvalnogo intelektu. L’viv, Vyd-vo L’vivs’koi’ politehniky, 2016, 236 p. ISBN: 978-617-607-902-6
Hoeppner F., Klawonn, F. Fuzzy clustering of sampled functions, In: Proc. 19th Int. Conf. of the North American Fuzzy Information Processing Society (NAFIPS). Atlanta, USA. 2000, pp. 251–255. DOI: 10.1109/NAFIPS. 2000.877431
Mashtalir S., Mashtalir V., Stolbovyi M. Video shot
boundary detection via sequential clustering, International Journal “Information Theories and Applications”, 2017, Vol. 24, Number 1, pp. 50–59.
Mantula E. V., Mashtalir S. V. Matrichnaja
prognozirujushhaja model’ i ee obuchenie v zadache
jekologicheskogo monitoringa, Jelektrotehnicheskie i
komp’juternye sistemy, 2013, No. 10(86), pp. 152–156.
Bodjanskij E. V., Pliss I. P. O reshenii zadachi upravlenija matrichnym ob’ektom v uslovijah neopredelennosti, Avtomatika i telemehanika, 1990, No. 2, pp. 175–178.
Chuev Ju. V., Mihajlov Ju. B., Kuz’min V. I. Prognozirovanie kolichestvennyh harakteristik processov. Moscow, Sov. Radio, 1975, 400 p.
Trigg D. W., Leach A. G. Exponential smoothing with an adaptive response rate, Operational Research Quarterly, 1967, 18, No. 1, pp. 53–59.
Mashtalir S. V. Mnogomernoe jeksponencial’noe sglazhivanie v zadachah analiza videodannyh, VI Mіzhnarodna shkola-semіnar «Teorіja prijnjattja rіshen’». Pracі shkolisemіnaru. Uzhgorod, UzhNU, 2012, pp. 136–137.
Downloads
How to Cite
Issue
Section
License
Copyright (c) 2019 S.V. Mashtalir, M.I. Stolbovyi
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
Creative Commons Licensing Notifications in the Copyright Notices
The journal allows the authors to hold the copyright without restrictions and to retain publishing rights without restrictions.
The journal allows readers to read, download, copy, distribute, print, search, or link to the full texts of its articles.
The journal allows to reuse and remixing of its content, in accordance with a Creative Commons license СС BY -SA.
Authors who publish with this journal agree to the following terms:
-
Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License CC BY-SA that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
-
Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
-
Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work.