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Cluster Based Approaches for Keyframe Selection in Natural Flower Videos

  • D. S. Guru
  • V. K. Jyothi
  • Y. H. Sharath Kumar
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 736)

Abstract

The selection of representative keyframes from a natural flower video is an important task in archival and retrieval of flower videos. In this paper, we propose an algorithmic model for automatic selection of keyframes from a natural flower video. The proposed model consists of two alternative methods for keyframe selection. In the first method, K-means clustering is applied to the frames of a given video using color, gradient, texture and entropy features. Then the cluster centroids are considered to be the keyframes. In the second method, the frames are initially clustered through Gaussian Mixture Model (GMM) using entropy features and the K-means clustering is applied on the resultant clusters to obtain keyframes. Among the two different sets of keyframes generated by two alternative methods, the one with a high fidelity value is chosen as the final set of keyframes for the video. Experimentation has been conducted on our own dataset. It is observed that the proposed model is efficient in generating all possible keyframes of a given flower video.

Keywords

Keyframe selection Clustering Gaussian Mixture Model K-means clustering Retrieval of flower videos 

References

  1. 1.
    de Avila, S.E.F., Ana, P., Antonia, D.: VSUMM: a mechanism designed to produce static video summaries and a novel evaluation method. Pattern Recogn. Lett. 32, 56–68 (2011)CrossRefGoogle Scholar
  2. 2.
    Chatzigiorgaki, M., Skodras, A.N.: Real-time keyframe extraction towards video content identification. IEEE, ISSN: 978-1-4244-3298 (2009)Google Scholar
  3. 3.
    Chen, W., Tian, Y., Wang, Y., Huang, T.: Fixed-point Gaussian mixture model for analysis-friendly surveillance video coding. Comput. Vis. Image Underst. 142, 65–79 (2016)CrossRefGoogle Scholar
  4. 4.
    Chuen-Horng, L., Chun-Chieh, C., Hsin-Lun, L., Jan-ray, L.: Fast k-means algorithm based on a level histogram for image retrieval. Expert Syst. Appl. 41, 3276–3283 (2014)CrossRefGoogle Scholar
  5. 5.
    Das, M., Manmatha, R., Riseman, E.M.: Indexing flower patent images using domain knowledge. IEEE Intell. Syst. 14(5), 24–33 (1999)CrossRefGoogle Scholar
  6. 6.
    Duda, R.O., Hart, P.E., Stork, D.G.: Unsupervised learning and clustering. Pattern Classification. Springer, New York (2001)Google Scholar
  7. 7.
    Gianluigiand, C., Raimondo, S.: An innovative algorithm for key frame extraction in video summarization. J. Real-Time Image Proc. 1, 69–88 (2006)CrossRefGoogle Scholar
  8. 8.
    Guru, D.S., Sharath, Y.H., Manjunath, S.: Texture features and KNN in classification of flower images. IJCA Special Issue on Recent Trends Image Process. Pattern Recogn. 1, 21–29 (2010)Google Scholar
  9. 9.
    Guru, D.S., Sharath, Y.H., Manjunath, S.: Textural features in flower classification. Math. Comput. Model. 54, 1030–1036 (2011)CrossRefGoogle Scholar
  10. 10.
    Haralick, R.M., Shanmugam, K., Dinstein, I.: Textural features for image classification. IEEE Trans. Syst. Man Cybern. SMC-3(6), 610–621 (1973)CrossRefGoogle Scholar
  11. 11.
    Sun, J., Zhang, X., Cui, J., Zhou, L.: Image retrieval based on color distribution entropy. Pattern Recogn. Lett. 27, 1122–1126 (2006)CrossRefGoogle Scholar
  12. 12.
    Loannidis, A., Chasanis, V., Likas, A.: Weighted multi-view key frame extraction. Pattern Recogn. Lett. 72, 52–61 (2016)CrossRefGoogle Scholar
  13. 13.
    Manjunath, S.: VARS: Video Archival and Retrieval System. Ph. D Thesis (2012)Google Scholar
  14. 14.
    Naveed, E., Tayyab, B.T., Sung, W.B.: Adaptive key frame extraction for video summarization using an aggregation mechanism. J. Vis. Commun. Image R. 23, 1031–1040 (2012)CrossRefGoogle Scholar
  15. 15.
    Nilsback, M.E., Zisserman, A.: A Visual vocabulary for flower classification. In: Proceedings of Computer Vision and Pattern Recognition, vol. 2, pp. 1447–1454 (2006)Google Scholar
  16. 16.
    Padmavathi, M., Yong, R., Yelena, Y.: Keyframe-based video summarization using delaunay clustering. Int. J. Digit. Librar. 6(2), 219–232 (2006)CrossRefGoogle Scholar
  17. 17.
    Kaunar, S.K., Panda, R., Chowdhury, A.S.: Video key frame extraction through dynamic delaunay clustering with a structural constraint. J. Vis. Commun. Image Rep. 24, 1212–1227 (2013)CrossRefGoogle Scholar
  18. 18.
    Song, G.H., Ji, Q.G., Lu, Z.M., Fang, Z.D., Xie, Z.H.: A novel video abstract method based on fast clustering of the region of interest in key frames. Int. J. Elec. Commun. 68, 783–794 (2014)CrossRefGoogle Scholar
  19. 19.
    Tremeau, A., Tominaga, S., Plataniotis, K.N.: Color in image and video processing: most recent trends and future research direction. EURASIP J. Image Video Process. 2008(3), 1–26 (2008)Google Scholar
  20. 20.
    Zeng, X., Hu, W., Li, W., Zhang, X., Xu, B.: Key-frame extraction using dominant – set clustering. In: Proceedings of IEEE International Conference on Multimedia and Expo, Hannover, Germany, pp. 1285–1288 (2008)Google Scholar
  21. 21.
    Zhou, H., Sadka, A.H., Swash, A.H., Azizi, J., Sidiq, U.A.: Feature extraction and clustering for dynamic video summarization. Neurocomputing 73, 1718–1729 (2010)CrossRefGoogle Scholar
  22. 22.
    Jyothi, V.K., Sharath, Y.H., Guru, D.S.: Sequential approach for key frame selection in natural flower videos. In: 6th International Conference on Signal and Image Processing (ICSIP) (2017, accepted)Google Scholar
  23. 23.
    Sheena, C.V., Narayan, N.K.: Key-frame extraction by analysis of histograms of video frames using statistical methods. Procedia Comput. Sci. 70, 36–40 (2015). 4th International Conference on Eco-friendly Computing and Communication SystemsCrossRefGoogle Scholar
  24. 24.
    Liu, H., Hao, H.: Key frame extraction based on improved hierarchical clustering algorithm. In: 11th International Conference on FSKD, pp. 793–797. IEEE Xplore (2014)Google Scholar

Copyright information

© Springer International Publishing AG, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Department of Studies in Computer ScienceUniversity of MysoreMysoreIndia
  2. 2.Department of Information ScienceMaharaja Institute of Technology Mysore (MITM)MandyaIndia

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