Abstract
With the continuous growth in the space of information sharing over the Internet, the sharing of video information is also growing. The video information is highly appreciable for the online distance education, security, and distance healthcare, interactive communication over the Internet and also in multimedia news systems. The information captured in the video format demands summarization for effective processing and efficient storage. In order to summarize any video information, the best possible strategy is extracting key frames from videos. A number of research attempts are made in order to establish the most efficient key frame extraction framework. Nonetheless, most of the parallel research outcomes are affected by either high or low key frame extractions. Thus, the demand from the modern research is to build an optimal framework to extract key frames from motion videos. The major challenges are identified in this work and addressed in the finest way possible. This work demonstrates the framework for few different cases such as object in motion, camera in motion or both in case of highly colour contrast video sequences. The results of this framework demonstrate lowest time complexity and higher level of information preservation compared to the parallel research outcomes.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
P. Aigrain, H. Zhang, D. Petkovic, Content-based representation and retrieval of visual media: a state-of-the-art review. Multimedia Tools Appl. 3, 179–202 (1996)
H.J. Zhang, J.Y.A. Wang, Y. Altunbasak, Content-based video retrieval and compression: a united solution, in Proceeding of IEEE International Conference on Image Processing, vol. 1, pp. 13–16 (1997)
T. Liu, H.-J. Zhang, Q. Feihu, A novel video key-frame-extraction algorithm based on perceived motion energy model. IEEE Trans. Circuits Syst. Video Technol. 13(10) (2003)
G. Liu, J. Zhao, Key frame extraction from MPEG video stream, in Second Symposium International Computer Science and Computational Technology (ISCSCT’09), Huangshan, P. R. China, pp. 007–011, 26–28 Dec 2009
U. Gargi, R. Kasturi, S.H. Strayer, Performance characterization of video-shot-change detection methods. IEEE Trans. Circuits Syst. Video Technol. 10(1) (2000)
Y. Zhuangt, Y. Rui, T.S. Huang, S. Mehrotra, Adaptive key frame extraction using unsupervised clustering, in Proceeding of IEEE International Conference on Image Processing (1998)
M. Mentzelopoulos, A. Psarrou, KeyFrame extraction algorithm using entropy difference, in Proceedings of the 6th ACM SIGMM International Workshop on Multimedia Information Retrieval, MIR, New York, NY, USA, 15–16 Oct 2004
Z. Rasheed, M. Shah, Detection and representation of scenes videos. IEEE Trans. Multimedia 7(6) (2005)
W. Wolf, Key frame selection by motion analysis, in Proceeding of IEEE International Conference on Acoustics, Speech Signal Processing, vol. 2, pp. 1228–1231 (1996)
T. Liu, H.-J. Zhang, F. Qi, A novel video key-frame-extraction algorithm based on perceived motion energy model. IEEE Trans. Circuits Syst. Video Technol. 13(10) (2003)
G.-C. Chao, Y.-P. Tsai, S.-K. Jeng, Augmented 3-D keyframe extraction for surveillance videos. IEEE Trans. Circuits Syst. Video Technol. 20(11) (2010)
K.-W Sze, K.-M. Lam, G. Qiu, A new key frame representation for video segment retrieval. IEEE Trans. Circuits Syst. Video Technol. 15(9) (2005)
H.S. Chang, S. Sull, S.U. Lee, Efficient video indexing scheme for content-based retrieval. IEEE Trans. Circuits Syst. Video Technol. 9(8) (1999)
J. Luo, C. Papin, K. Costello, Towards extracting semantically meaningful key frames from personal video clips: from humans to computers. IEEE Trans. Circuits Syst. Video Technol. 19(2) (2009)
C. Dang, H. Radha, RPCA-KFE: key frame extraction for video using robust principal component analysis. IEEE Trans. Image Process. 24(11) (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Mallikharjuna Lingam, K., Reddy, V.S.K. (2019). Key Frame Extraction Using Content Relative Thresholding Technique for Video Retrieval. In: Wang, J., Reddy, G., Prasad, V., Reddy, V. (eds) Soft Computing and Signal Processing . Advances in Intelligent Systems and Computing, vol 900. Springer, Singapore. https://doi.org/10.1007/978-981-13-3600-3_78
Download citation
DOI: https://doi.org/10.1007/978-981-13-3600-3_78
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-3599-0
Online ISBN: 978-981-13-3600-3
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)