Abstract
With the high availability of digital video contents on the internet, users need more assistance to access digital videos. Various researches have been done about video summarization and semantic video analysis to help to satisfy these needs. These works are developing condensed versions of a full length video stream through the identification of the most important and pertinent content within the stream. Most of the existing works in these areas are mainly focused on event mining. Event mining from video streams improves the accessibility and reusability of large media collections, and it has been an active area of research with notable recent progress. Event mining includes a wide range of multimedia domains such as surveillance, meetings, broadcast, news, sports, documentary, and films, as well as personal and online media collections. Due to the variety and plenty of Event mining techniques, in this paper we suggest an analytical framework to classify event mining techniques and to evaluate them based on important functional measures. This framework could lead to empirical and technical comparison of event mining methods and development of more efficient structures at future.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.References
Blum A, Mitchell T (1998) Combining labeled and unlabeled data with co-training. In: Proceedings of the workshop on computational learning theory
Brand M, Oliver N, Pentland A (1997) Coupled hidden Markov models for complex action recognition. In: Proceedings of the IEEE computer society conference computer vision and pattern recognition (CVPR ’97). Washington, DC, p 994
Cisco Visual Networking Index: Forecast and Methodology. (2009–2014) CISCO (2010)
Dai K, Zhang J, Li G (2006) Video mining: concepts, approaches and applications. 2006 IEEE
Fan J., Elmagarmid AK, Zhu X., Aref WG., Wu L (2004) Classview: hierarchical video shot classification, indexing and accessing. IEEE Trans. Multimedia 6: 70–86
Fernndez C, Baiget P, Roca FX, Gonzlez J (2010) Determining the best suited semantic events for cognitive surveillance. Computer Vision Center, UAB, Edifici O, Campus UAB, 08193 Barcelona, Spain. (Elsevier)
Francois ARJ, Nevatia R, Hobbs J, Bolles RC (2005) Verl: an ontology framework for representing and annotating video events. IEEE MultiMedia 12(4): 76–86
Geetha P., Narayanan V (2008) A survey of content-based video retrieval. J Comput Sci 4(6): 474–486 ISSN 1549-3636
Hastie T, Tibshirani R, Friedman J et al (2001) The elements of statistical learning: data mining, inference, and prediction. Springer, New York
Jain AK, Duin RPW, Mao J (2000) Statistical pattern recognition: a review. IEEE Trans Pattern Anal Mach Intell 22(1): 4–37
Kolekar MH, Palaniappan K (2009) Semantic concept mining based on hierarchical event detection for Soccer video indexing. J Multimedia 4(5): 298–312
Lin C, Tseng B, Smith J (2003) VideoAnnEx: IBM MPEG-7 annotation tool for multimedia indexing and concept learning. In: IEEE international conference on multimedia and expo. Baltimore, MD
Money AG., Agius H (2008) Video summarization: a conceptual framework and survey of the state of the art. J Commun Image Recognit 19: 121–143 (Elsevier)
Naphade M, Huang T (2002) Discovering recurrent events in video using unsupervised methods. In: Proceedings of the IEEE international conference on image processing. Rochester, NY
Nevatia R, Zhao T, Hongeng S (2003) Hierarchical language-based representation of events in video streams. In: Proceedings of the IEEE workshop event mining (event ’03), 2003
Ngo C-W, Pong T-C, Zhang H-J (2003) Motion analysis and segmentation through spatio-temporal slices processing. IEEE Trans Image Process 12: 341–355
Nigam K, Ghani R (2000) Analyzing the effectiveness and applicability of co-training. In: Proceedings of CIKM, pp 86–93
Oliver NM., Rosario B., Pentland A (2000) Bayesian computer vision system for modeling human interactions. IEEE Trans Pattern Anal Machine Intell 22(8): 831–843
Roush W (2007) Tr10: peering into video’s future. Technol. Rev. [Online]. Available: http://www.technologyreview.com/Infotech/18284/?a=f
Tu Z (2007) Learning generative models via discriminative approaches. Laboratory of Neuro Imaging, UCLA
Tziakos I., Cavallaro A., Xu L-Q (2010) Event monitoring via local motion abnormality detectionin non-linear subspace. Neurocomputing 73: 1881–1891 (Elsevier)
Velipasalar S, Brown LM, Hampapur A (2006) Specifying, interpreting and detecting high-level, spatio-temporal composite events in single and multi-camera systems. In: Proceedings of the IEEE computer society conference on computer vision and pattern recognition workshop. Washington, DC, p 110
Xie L, Yan R (2009) Extracting semantics from multimedia content: challenges and solutions. Signals Commun Technol, 1–31. doi:10.1007/978-0-387-76569-3_2
Xie L., Sundaram H., Campbell M (2008) Event mining in multimedia streams. Proc IEEE 96(4): 623–647
Yu H., Han J., Chen-Chuan CK (2004) PEBL: Web page classification without negative examples. IEEE Trans Knowl Data Eng 16: 70–81
Zhu X (2005) Semi-supervised learning literature survey. Computer Science, University of Wisconsin-Madison, Madison. Technical report 1530
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Koohzadi, M., Keyvanpour, M.R. An analytical framework for event mining in video data. Artif Intell Rev 41, 401–413 (2014). https://doi.org/10.1007/s10462-012-9315-5
Published:
Issue Date:
DOI: https://doi.org/10.1007/s10462-012-9315-5