CFP last date
20 December 2024
Call for Paper
January Edition
IJCA solicits high quality original research papers for the upcoming January edition of the journal. The last date of research paper submission is 20 December 2024

Submit your paper
Know more
The week's pick
Responsive image
Improved Shuffled Frog Leaping Algorithm with Self-Adaptive Shuffling for Fuzzy Logic PD+G Controller Optimization in Robotic Manipulators

Duc Hoang Nguyen
Random Articles
Reseach Article

Video Events Extraction based on Mixed Feature Modeling

by B. S. Daga, A. A. Ghatol, Anuprita Daga
journal cover thumbnail

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Volume 157 - Number 10
Year of Publication: 2017
Authors: B. S. Daga, A. A. Ghatol, Anuprita Daga
10.5120/ijca2017912685

B. S. Daga, A. A. Ghatol, Anuprita Daga . Video Events Extraction based on Mixed Feature Modeling. International Journal of Computer Applications. 157, 10 ( Jan 2017), 22-29. DOI=10.5120/ijca2017912685

@article{ 10.5120/ijca2017912685,
author = { B. S. Daga, A. A. Ghatol, Anuprita Daga },
title = { Video Events Extraction based on Mixed Feature Modeling },
journal = { International Journal of Computer Applications },
issue_date = { Jan 2017 },
volume = { 157 },
number = { 10 },
month = { Jan },
year = { 2017 },
issn = { 0975-8887 },
pages = { 22-29 },
numpages = {9},
url = { https://ijcaonline.org/archives/volume157/number10/26867-2017912685/ },
doi = { 10.5120/ijca2017912685 },
publisher = {Foundation of Computer Science (FCS), NY, USA},
address = {New York, USA}
}
%0 Journal Article
%1 2024-02-07T00:03:33.459900+05:30
%A B. S. Daga
%A A. A. Ghatol
%A Anuprita Daga
%T Video Events Extraction based on Mixed Feature Modeling
%J International Journal of Computer Applications
%@ 0975-8887
%V 157
%N 10
%P 22-29
%D 2017
%I Foundation of Computer Science (FCS), NY, USA
Abstract

Now a day’s increase in access of video-based application has opened need for extracting the content in videos. Unprocessed data and low-level features alone are not sufficient to complete the user’s need so deeper understanding of the content at the semantic level is required. Currently, manual techniques which are inefficient, subjective and expensive in time and limit the querying capabilities are used to fulfill the gap between low-level representative features and high-level semantic content. The system that allows the user to query and retrieve associated objects, events, and concepts automatically is proposed .The events can also be representative objects, actions, their impressions, and so on. Here an ontology-based video semantic content model which uses spatial/temporal relations in event and concept definitions is leveraged. Simple & efficient process consideration on main object detection & its common associated mixed direct measurable feature like shape, texture & derived features like co-occurrence & topology is evaluated. An ontology definition provides a wide-domain applicable rule construction standard. In addition to domain ontology’s, additional rule definitions to lower spatial relation computation cost are used. This leads to describe some complicated events close to human thinking. The proposed system has been implemented and tested on domains like road accident & sports for precision and recall measures.

References
  1. M. Petkovic and W. Jonker, “An Overview of Data Models and Query Languages for Content-Based Video Retrieval,” Proc. Int’l Conf. Advances in Infrastructure for E-Business, Science, and Education on the Internet, Aug. 2000.
  2. M. Petkovic and W. Jonker, “Content-Based Video Retrieval by Integrating Spatio-Temporal and Stochastic Recognition of Events,” Proc. IEEE Int’l Workshop Detection and Recognition of Events in Video, pp. 75-82, 2001.
  3. L.S. Davis, S. Fejes, D. Harwood, Y. Yacoob, I. Haratoglu, and M.J. Black, “Visual Surveillance of Human Activity,” Proc. Third Asian Conf. Computer Vision (ACCV), vol. 2, pp. 267-274, 1998.
  4. Omar Kettani, Benaissa Tadili, Faycal Ramdani “A Deterministic K-means Algorithm based on Nearest Neighbor Search” International Journal of Computer Applications (0975 – 8887) Volume 63– No.15, February 2013
  5. B. S. Daga, and A. A. Ghatol, “Detection of Objects and Activities in videos using Spatial Relations and Ontology based approach in Video Database System” International Journal of Advances in Engineering and Technology, Volume 9, Issue 6, pp. 640-650, December 2016, ISSN NUMBER: 22311963
  6. A.Hakeem and M. Shah, “Multiple Agent Event Detection and Representation in Videos,” Proc. 20th Nat’l Conf. Artificial Intelligence (AAAI), pp. 89-94, 2005.
  7. B.V.Patel, B.S.Daga, B.B.Meshram ,“Building Multimedia Applications” International Journal on Computer Engineering & Information Technology, Vol. 14, no.19, pp. 10-15, 2010 ISSN NUMBER: 0974-2034
  8. T. Sevilmis, M. Bastan, U. Guido¨ bay, and O ¨ Ulusoy, “Automatic Detection of Salient Objects and Spatial Relations in Videos for a Video Database System,” Image Vision Computing, vol. 26, no. 10, pp. 1384-1396, 2008.
  9. B. S. Daga, and A. A. Ghatol, “Multicue Optimized Object Detection for Automatic Video Event Extraction”, International Journal of Science and Technology, Volume 9( 47), December 2016, ISSN NUMBER: 0974-6846
  10. Huangzhou, Zhejiang, China International Conference on “Image Analysis and Signal Processing - Video shot segmentation and key frame extraction based on SIFT feature ”IEEE 2012 International Conference on Image Analysis and Signal Processing (IASP).
  11. “Feature extraction for human action classification using adaptive key frame interval,” IEEE 2014 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA) - Chiang Mai, Thailand.
  12. L. Bai, S.Y. Lao, G. Jones, and A.F. Smeaton, “Video Semantic Content Analysis Based on Ontology,” IMVIP ’07: Proc. 11th Int’l Machine Vision and Image Processing Conf., pp. 117-124, 2007.
  13. R. Nevatia and P. Natarajan, “EDF: A Framework for Semantic Annotation of Video,” Proc. 10th IEEE Int’l Conf. Computer Vision Workshops (ICCVW ’05), p. 1876, 2005.
  14. A.D. Bagdanov, M. Bertini, A. Del Bimbo, C. Torniai, and G. Serra, “Semantic Annotation and Retrieval of Video Events Using Multimedia Ontologies,” Proc. IEEE Int’l Conf. Semantic Computing (ICSC), Sept. 2007.
  15. R. Nevatia, J. Hobbs, and B. Bolles, “An Ontology for Video Event Representation,” Proc. Conf. Computer Vision and Pattern Recognition Workshop, p. 119, http://ieeexplore.ieee.org/xpls/abs_all. Jsp renumber=1384914, 2004.
  16. U. Akdemir, P.K. Turaga, and R. Chellappa, “An Ontology Based Approach for Activity Recognition from Video,” Proc. ACM Int’l Conf. Multimedia, A. El-Saddik, S. Vuong, C. Griwodz, A.D. Bimbo, K.S. Candan, and A. Jaimes, eds., pp. 709-712, http://dblp.unitrier.de/db/conf/mm/mm2008.html#AkdemirTC08, 2008.
  17. M. Abinaya, R. Kiruthiga, A. Kiruthika “Automatic Mining in Ontology Based Fuzzy Video Semantic Content Model” International Journal of Communication and Computer Technologies Volume 02 – No.15 Issue: 02 March 2014 ISSN NUMBER: 2278-9723
  18. T. Yilmaz, “Object Extraction from Images/Videos Using a Genetic Algorithm Based Approach,” master’s thesis, Computer Eng. Dept., METU, Turkey, 2008.
  19. Y. Yildirim and A. Yazici, “Ontology-Supported Video Modeling and Retrieval,” Proc. Fourth Int’l Conf. Adaptive Multimedia Retrieval: User, Context, and Feedback (AMR), pp. 28- 41, 2006.
  20. Y. Yildirim, T. Yilmaz, and A. Yazici, “Ontology-Supported Object and Event Extraction with a Genetic Algorithms Approach for Object Classification,” Proc. Sixth ACM Int’l Conf. Image and Video Retrieval (CIVR ’07), pp. 202-209, 2007.
  21. V. Mezaris, I. Kompatsiaris, N.V. Boulgouris, and M.G. Strintzis, “Real-Time Compressed-Domain Spatiotemporal Segmentation and Ontologies for Video Indexing and Retrieval,” IEEE Trans. Circuits Systems Video Technology, vol. 14, no. 5, pp. 606-621, May 2004.
  22. Huayong Liu; Tao Li, “Key frame extraction based on improved frame blocks features and second extraction,” IEEE 2015 12th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)2015.8.15-2015.8.17
  23. W. Chen and D.S. Warren, “C-logic of Complex Objects,” PODS ’89: Proc. Eighth ACM SIGACT-SIGMOD-SIGART Symp. Principles of Database Systems, pp. 369-378, 1989.
  24. J.F. Allen, “Maintaining Knowledge about Temporal Intervals,” Comm. ACM, vol. 26, no. 11, pp. 832-843, 1983.
  25. Nameirakpam Dhanachandra,, Khumanthem Manglem and Yambem Jina Chanu, “Image Segmentation using K-means Clustering Algorithm and Subtractive Clustering Algorithm,” Eleventh International Multi-Conference on Information Processing-2015 (IMCIP-2015).
  26. M. Vazirgiannis, “Uncertainty Handling in Spatial Relationships,” SAC ’00: Proc. ACM Symp. Applied Computing, pp. 494- 500, 2000.
  27. P.-W. Huang and C.-H. Lee, “Image Database Design Based on 9D-SPA Representation for Spatial Relations,” IEEE Trans. Knowledge and Data Eng., vol. 16, no. 12, pp. 1486-1496, Dec. 2004
  28. P. Pedda Sadhu Naik, T. Venu Gopal, “A Novel Approach for Color Image Segmentation Using Iterative Partitioning Mean Shift Clustering Algorithm” IEEE ICCSP 2015 conference 978-1-4799-8081-9/15/$3l.00 © 2015 IEEE.
  29. “Portege´Ontology Editor,” http://protege.stanford.edu/, 2012.
  30. C. Xu, J. Wang, K. Wan, Y. Li, and L. Duan, “Live Sports Event Detection Based on Broadcast Video and Web-Casting Text,” MULTIMEDIA ’06: Proc. 14th Ann. ACM Int’l Conf. Multimedia, pp. 221-230, 2006.
  31. Xiang Zhai, “The Key Event Extraction Algorithm Based on Shot Events in Soccer Video,” International Journal of Multimedia and Ubiquitous Engineering, Vol. 11 No. 1, pp. 33-42, 2016. ISSN No – 1975-0080
Index Terms

Computer Science
Information Sciences

Keywords

Video Content Modeling Multicue Spatio-temporal Event Detection Event Extraction

Powered by PhDFocusTM