Abstract
The exponential growth of video data produced by surveillance cameras, cell phones and movie post-production creates the need to process big-data using methods that are able to produce instantaneous result. Video summarization can be accomplished and represented in several manners. The achieved summaries might be a sequence of images or short videos. In our method, an input video is divided into segments. From each segment we calculate key frames using three different key frame definitions, to summarize the video data. The contribution of this paper is to describe how to incorporate techniques that extract on the fly results.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Hu, W., Xie, N.: A survey on visual content based video indexing and retrieval. IEEE Transactions on Systems, Man, and Cybernetics 41(6), 797–819 (2011)
Cotsaces, C., Nikolaidis, N., Pitas, I.: Video shot boundary detection and condensed representation: A review. IEEE Signal Processing Magazine 23(2), 28–37 (2006)
Cernekova, Z., Pitas, I., Nikou, C.: Information theory-based shot cut/fade detection and video summarization. IEEE Transactions on Circuits and Systems for Video Technology 16 (January 2006)
Opencv metrics for histograms, http://docs.opencv.org/modules/imgproc/doc/histograms.html?highlight=comparehist#comparehist
Smoliar, S.W., Zhang, H.J., Kankanhalli, A.: Automatic partitioning of full-motion video. ACM Multimedia Syst. 1(1), 10–28 (1993)
Cernekova, Z., Kotropoulos, C., Pitas, I.: Video shot segmentation using singular value decomposition. SPIE Journal of Electronic Imaging 16(4) (December 2007)
Chen, Y.K., Holliman, M., Debes, E., Zheltov, S., Knyazev, A., Bratanov, S., ... Santos, I.: Media Applications on Hyper-Threading Technology. Journal Intel Technology 6(1) (2002)
Pitas, I., Venetsanopoulos, A.: Nonlinear Digital Filters: Principles and Applications. Kluwer Academic (1990)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2014 Springer International Publishing Switzerland
About this paper
Cite this paper
Mpountouropoulos, N., Tefas, A., Nikolaidis, N., Pitas, I. (2014). Visual Information Analysis for Big-Data Using Multi-core Technologies. In: Pan, JS., Snasel, V., Corchado, E., Abraham, A., Wang, SL. (eds) Intelligent Data analysis and its Applications, Volume I. Advances in Intelligent Systems and Computing, vol 297. Springer, Cham. https://doi.org/10.1007/978-3-319-07776-5_32
Download citation
DOI: https://doi.org/10.1007/978-3-319-07776-5_32
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-07775-8
Online ISBN: 978-3-319-07776-5
eBook Packages: EngineeringEngineering (R0)