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Video Key-Frame Extraction using Unsupervised Clustering and Mutual Comparison
Nitin J. Janwe, Kishor K. Bhoyar
Pages - 73 - 84     |    Revised - 31-05-2016     |    Published - 30-06-2016
Volume - 10   Issue - 2    |    Publication Date - June 2016  Table of Contents
Key-frame Extraction, Semantic Concept Based Video Retrieval, HSV Histogram, GLCM Texture.
Key-frame extraction is one of the important steps in semantic concept based video indexing and retrieval and accuracy of video concept detection highly depends on the effectiveness of keyframe extraction method. Therefore, extracting key-frames efficiently and effectively from video shots is considered to be a very challenging research problem in video retrieval systems. One of many approaches to extract key-frames from a shot is to make use of unsupervised clustering. Depending on the salient content of the shot and results of clustering, key-frames can be extracted. But usually, because of the visual complexity and/or the content of the video shot, we tend to get near duplicate or repetitive key-frames having the same semantic content in the output and hence accuracy of key-frame extraction decreases. In an attempt to improve accuracy, we proposed a novel key-frame extraction method based on unsupervised clustering and mutual comparison where we assigned 70% weightage to color component (HSV histogram) and 30% to texture (GLCM), while computing a combined frame similarity index used for clustering. We suggested a mutual comparison of the key-frames extracted from the output of the clustering where each key-frame is compared with every other to remove near duplicate keyframes. The proposed algorithm is both computationally simple and able to detect non-redundant and unique key-frames for the shot and as a result improving concept detection rate. The efficiency and effectiveness are validated by open database videos.
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1 J. Son, H. Lee, and H. Oh. “PVR: a novel PVR scheme for content protection.” IEEE Transactions on Consumer Electronics, vol. 57, no. 1, pp. 173-177, 2011.
2 M. Naphade, A. Ferman, and et al. “A high performance algorithm for shot boundary detection using multiple cues.” In Proc. ICIP, Chicago, Oct. 1998, pp. 884-887, vol.1.
3 J. Boreczky and L.Rowe. “Comparison of video shot boundary detection techniques.” Journal of Electronic Imaging, 5(2), pp. 122-128, Apr. 1996.
4 H. Zhang, J. Wang, and Y. Altunbasak. “Content-based video retrieval and compression: A unified solution.” in Proc. IEEE Int. Conf. on Image Proc., 1997, pp. 13-16, vol.1.
5 N. Janwe and K. Bhoyar. “Video Shot Boundary Detection based on JND Histogram.” In Proc. of ICIP, Image Information Processing, Second IEEE conference on, 2013, pp. 476- 480.
6 I. Koprinska, S. Carrato. “Temporal video segmentation: A Survey.” Signal processing: Image Communication, vol. 16, no. 5, pp. 477-500, 2001.
7 J. Mas and G. Fernandez. “Video shot boundary detection based on color histogram.” Digital Television Center (CeTVD) La Salle School of Engineering, Ramon Llull Univ., Barcelona, Spain, In TREC2003 Video Track.
8 R. Haralick, K.Shanmugam, I. Dinstein. “Textural features for image classification.” IEEE Trans. Systems Man Cybernet. SMC-3, pp. 610 – 621, 1973.
9 G. LaxmiPriya, S.Domnic. “Transition detection using Hilbert transform and texture features.” American Journal of Signal Processing, vol. 2 (2), pp. 35-40, 2012.
10 J. Boreczky and L. Rowe. “Comparison of video shot boundary detection techniques.” In Storage and Retrieval for Image and Video Databases (SPIE), vol. 2670, pp.170-179, 1996.
11 K. Sze, K. Lam, and G. Qiu. “A new key frame representation for video segment retrieval.” IEEE Trans. Circuits Syst. Video Technol., vol.15, no.9, pp.1148–1155, Sep.2005.
12 B. Truong and S. Venkatesh. “Video abstraction: A systematic review and classification.” ACM Trans. Multimedia Comput., Commun. Appl., vol.3, no.1, art. 3, pp.1–37, Feb.2007.
13 H. Zhang, J. Wu, D. Zhong, and S. Smoliar. “An integrated system for content-based video retrieval and browsing.” Pattern Recognit., vol.30, no.4, pp.643–658, 1997.
14 X. Zhang, T. Liu, K. Lo, and J. Feng. “Dynamic selection and effective compression of key frames for video abstraction.” Pattern Recognit. Lett., vol.24, no.9–10, pp.1523–1532, Jun. 2003.
15 A. Ferman and A. Tekalp. “Two-stage hierarchical video summary extraction to match low-level user browsing preferences.” IEEE Trans. Multimedia, vol.5, no.2, pp.244–256, Jun. 2003.
16 X. Yu, L. Wang, Q. Tian, and P. Xue. “Multilevel video representation with application to keyframe extraction.” In Proc. Int. Multimedia Modelling Conf., 2004, pp.117–123.
17 A. Nagasaka and Y. Tanaka. “Automatic video indexing and full-video search for object appearances.” In Visual Database Systems II, 1992.
18 M. Furini, F. Geraci, M. Montangero, and M. Pellegrini. “STIMO: STIll and MOving video storyboard for the web scenario.” Multimedia Tools and Applications, vol.46, no.1, pp.47–69, 2010.
19 N. Ejaz, I. Mehmood, and S.Baik. “Efficient visual attention based framework for extracting key frames from videos.” Signal Processing: Image Communication, vol.28, pp.34–44, 2013.
20 G. Ciocca and R. Schettini. “An innovative algorithm for key frame extraction in video summarization.” Journal of Real-Time Image Processing, Mar. 2006, vol. 1, issue 1, pp. 69-88.
21 M. Mentzelopoulos, and Alexandra Psarrou. “KeyFrame Extraction Algorithm using Entropy Difference.” ACM MIR, pp. 39-45, Oct. 2004.
22 S. Hoon, K. Yoon, and I. Kweon. “A new Technique for Shot Detection and Key Frames Selection in Histogram Space.” Proc. 12th Workshop on Image Processing and Image Understanding, 2000, pp. 475-479.
23 A. Hanjalic, R. Lagendijk, J. Biemond. “A new Method for Key Frame Based Video Content Representation.” In Image Databases and Multimedia Search, World Scientific Singapore, 1998.
24 H. Chang, S. Sull, L. Sang. “Efficient Video Indexing Scheme for Content-Based Retrieval.” IEEE Trans. on Circuits and Systems for Video Technology, 1999, 9(8), pp. 1269-1279.
25 T. Liu., H. Zhang, F. Qi. “A novel video key-frame-extraction algorithm based on perceived motion energy model.” IEEE Trans. Circuits and Systems for Video Technology, 2003, 13(10), pp. 1006-1013.
26 S. Algur and R. Vivek,” Video Key Frame Extraction using Entropy value as Global and Local Feature.” arXiv:1605.08857 [cs.CV], 2016.
Mr. Nitin J. Janwe
Dept. of Information Technology, Yeshwantrao Chavan College of Engineering, Nagpur, India - India
Dr. Kishor K. Bhoyar
Dept. of Information Technology, Yeshwantrao Chavan College of Engineering, Nagpur, India - India