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PICS: A Novel Technique for Video Summarization

  • Gagandeep Singh
  • Navjot Singh
  • Krishan Kumar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 748)

Abstract

With brisk growth in video data the demand for both effective and powerful methods for video summarization is also elevated so that the users can browse the apace and comprehend a large amount of video content. Our paper highlights a novel keyframe extraction technique based on the clustering to attain video summarization (VS). Clustering is an unsupervised procedure and these algorithms rely on some prior assumptions to define subgroups in the given dataset. To extract the keyframes in a video, a procedure called k-medoids clustering is used. To find the number of optimal clusters, is a challenge here. This task can be achieved by using the cluster validation procedure, Calinski–Harabasz index (CH index). This procedure is based on the well-defined criteria for clustering that enable the selection of an optimal parameter value to get the best partition results for the dataset. Thus, CH index allows users a parameter independent VS approach to select the keyframes in video without bringing down the further computational cost. The quantitative and qualitative evaluation and the computational complexity are drained to compare the achievements of our proposed model with the state-of-the-art models. The experimental results on two standard datasets having various categories of videos indicate PICS model outperforms other existing models with best F-measure.

References

  1. 1.
    Singh, N., et al.: Performance enhancement of salient object detection using superpixel based Gaussian mixture model. MTAP, 1–19 (2017)Google Scholar
  2. 2.
    Kumar, K., et al.: Event BAGGING: a novel event summarization approach in multi-view surveillance videos. In: IEEE IESC’17 (2017)Google Scholar
  3. 3.
    Gao L, et al.,: Learning in high-dimensional multimedia data: the state of the art. Multimed. Syst. 1–11 (2017)Google Scholar
  4. 4.
    Kumar, K., et al.: Eratosthenes sieve based key-frame extraction technique for event summarization in videos. MTAP, 1–22 (2017)Google Scholar
  5. 5.
    Truong, B.T., Venkatesh, S.: Video abstraction: a systematic review and classification. ACM Trans. Multimed. Comput. Commun. Appl. 3(1, Article 3), 37 (2007).  https://doi.org/10.1145/1198302.1198305CrossRefGoogle Scholar
  6. 6.
    Vermaak, J., Perez, P., Gangnet, M., Blake, A.: Rapid summarization and browsing of video sequences. In: British machine vision conference, pp 1–10 (2002)Google Scholar
  7. 7.
    Zhuang, Y., Rui, Y., Huang, T.S., Mehrotra, S.: Adaptive key frame extraction using unsupervised clustering. In: Proceedings of the International Conference on Image Processing, vol. 1, pp 866–870. IEEE (1998)Google Scholar
  8. 8.
    Kumar, K., et al.: Equal partition based clustering approach for event summarization in videos. In: The 12th IEEE SITIS’16, pp. 119–126 (2016)Google Scholar
  9. 9.
    Hadi, Y., Essannouni, F., Thami, R.O.H.: Unsupervised clustering by k-medoids for video summarization. In: ISCCSP’06 (2006)Google Scholar
  10. 10.
    Hadi, Y., Essannouni, F., Thami, R.O.H. (2006). Video summarization by k-medoid clustering. In: Proceedings of the 2006 ACM Symposium on Applied Computing, pp. 1400–1401. ACM.  https://doi.org/10.1145/1141277.1141601
  11. 11.
    Anirudh, R., Masroor, A., Turaga, P.: Diversity promoting online sampling for streaming video summarization. In: 2016 IEEE International Conference on Image Processing (ICIP), pp. 3329–3333. IEEE (2016)Google Scholar
  12. 12.
    Kumar, K., Shrimankar, D.D.: F-DES: fast and deep event summarization. IEEE TMM (2017).  https://doi.org/10.1109/TMM.2017.2741423.
  13. 13.
    Kaufman, L., Rousseeuw, P.J.: Clustering by means of Medoids. In: Dodge, Y. (ed.) Statistical Data Analysis Based on the L1 - Norm and Related Methods, pp. 405–416. North- Holland, New York (1987)Google Scholar
  14. 14.
    Calinski, T., Harabasz, J.: A dendrite method for cluster analysis. Commun. Stat. 3(1), 1–27 (1974)Google Scholar
  15. 15.
    Van Craenendonck, T., Blockeel, H.: Using internal validity measures to compare clustering algorithms. In: Benelearn 2015 Poster presentations (online), pp. 1–8 (2015)Google Scholar
  16. 16.
    de Avila, S.E.F., Lopes, A.P.B., et al.: Vsumm: a mechanism designed to produce static video summaries and a novel evaluation method. Pattern Recognit. Lett. 32(1), 56–68 (2011)CrossRefGoogle Scholar
  17. 17.
    Furini, M., Geraci, F., Montangero, M., Pellegrini, M.: Stimo: still and moving video storyboard for the web scenario. Multimed. Tools Appl. 46(1), 47–69 (2010)CrossRefGoogle Scholar
  18. 18.
    Mundur, P., Rao, Y., Yesha, Y.: Keyframe-based video summarization using Delaunay clustering. Int. J. Digit. Libr. 6(2), 219–232 (2006)Google Scholar
  19. 19.
    Video open project storyboard (2016). https://open-video.org/results.php?size=extralarge

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Department of Computer Science and EngineeringNational Institute of Technology UttarakhandSrinagar (Garhwal)India

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