Adaptive Fibonacci Search Method of Video Key Frame Extraction
||International Journal of Computer Trends and Technology (IJCTT)||
|© 2019 by IJCTT Journal|
|Year of Publication : 2019|
|Authors : Baomin Shao, Hongyun Jia|
|DOI : 10.14445/22312803/IJCTT-V67I10P103|
MLA Style:Baomin Shao, Hongyun Jia "Adaptive Fibonacci Search Method of Video Key Frame Extraction," International Journal of Computer Trends and Technology 67.10 (2019):16-19.
APA Style Baomin Shao, Hongyun Jia. Adaptive Fibonacci Search Method of Video Key Frame ExtractionInternational Journal of Computer Trends and Technology, 67(10),16-19.
With the development of video techniques, one of the issues of building a proficient video processing system is to present a whole video with key frames to eliminate redundant information. This paper presented a novel video key frame extraction method using adaptive Fibonacci search algorithm. A pre-sampling was employed for selecting the suitable parameters for the sequence search process. After color and texture features were computed and combined, each frame was represented by a 92-dimensional feature vector, with the help of similarity measurement of frame combined feature vector, a divide-and-conquer searching method was executed. Experiments showed that this approach considerably reduced the processing time of each video while maintaining a similar precision and recall rates, at the same time, it could extract key frames from videos more effectively.
 D.B. Ponceleon, S. Srinivasan, A. Amir, D. Petkovic, D. Diklic, Key to effective video retrieval: effective cataloging and browsing, in: Proceedings of the ACM International Conference on Multimedia, 1998, pp. 99–107.
 B.T. Truong, S. Venkatesh, Video abstraction: a systematic review and classification, ACM Transactions on Multimedia Computing, Communications, and Applications 3 (1) (2007) 1–37.
 A.G. Money, H.W. Agius, Video summarization: a conceptual framework and survey of the state of the art, Journal of Visual Communication and Image Representation 19 (2) (2008) 121–143.
 S. Uchihashi and J. Foote, Summarizing video using a shot importance measure and a frame-packing algorithm, in IEEE ICASSP, 1999, vol. 6, pp. 3041-3044.
 Z. Rasheed and M. Shah, Detection and representation of scenes in videos, IEEE Trans. Multimedia, vol. 7, no. 6, pp. 1097-1105,Dec. 2005
 Y. Zhuang, Y. Rui, T. S. Huang and S. Mehrotra, Adaptive key frame extraction using unsupervised clustering, In Proceedings of IEEE International Conference on Image Processing (ICIP), 1998.
 Vasileios Chasanis, Aristidis Likas and Nikolaos Galatsanos,Video rushes summarization using spectral clustering and sequence alignment, Proceedings of the 2nd ACM TRECVid Video Summarization Workshop, 2008.
 G. Paschos, “Perceptually uniformcolor spaces for color texture analysis: an empirical evaluation,” IEEE Transactions on Image Processing, vol. 10, no. 6, pp. 932– 937, 2001.
 J. Almeida, N.J. Leite, R.S. Torres, VISON: video summarization for online applications, Pattern Recognition Letters 33 (4) (2012) 397–409.
 G. Paschos, Perceptually uniform color spaces for color texture analysis: an empirical evaluation, IEEE Transactions on Image Processing 10 (6) (2001) 932–937.
 B.S. Manjunath, J.R. Ohm, V.V. Vasudevan, A. Yamada, MPEG-7 color and texture descriptors, IEEE Transactions on Circuits and Systems for Video Technology 6 (11) (2000).
Key frame extraction, Video analysis, Fibonacci search, Image feature vector