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Cluster Computing

, Volume 22, Supplement 1, pp 1211–1218 | Cite as

Histogram difference with Fuzzy rule base modeling for gradual shot boundary detection in video cloud applications

  • A. Kethsy Prabavathy
  • J. Devi ShreeEmail author
Article
  • 145 Downloads

Abstract

In the field of shot boundary detection the fundamental step is video content analysis towards video indexing, summarization and retrieval as to be carried out for video cloud based applications. However, there are several beneficial in the previous work; reliable detection of video shot is still a challenging issue. In this paper the focus is carried out on the problem of gradual transition detection from video. The proposed approach is fuzzy-rule based system with gradual identification and a set of fuzzy rules are evaluated with dissolve and wipes (fad-in and fad-out) during gradual transition. First, extracting the features from the video frames then applying the fuzzy rules in to the frames for identifying the gradual transitions. The main advantage of the proposed method is its level of accuracy in the gradual detection getting increased. Also, the existing gradual detection algorithms are mainly based on the threshold component, but the proposed method is rule based. The proposed method is evaluated on variety of video sequences from different genres and compared with existing techniques from the literature. Experimental results proved for its effectiveness on calculating performance in terms of the precision and recall rates.

Keywords

Video analysis Video indexing Video cloud Fuzzy rule base Shot boundary detection Video retrieval Gradual transition 

References

  1. 1.
    Lo, C., Wang, S.: Video segmentation using a histogram-based fuzzy c-means clustering algorithm. Comput. Stand. Interfaces 23, 429–438 (2001)CrossRefGoogle Scholar
  2. 2.
    Liu, Xin, Dai, Jin: A method of video shot-boundary detection based on grey modeling for histogram sequence. Int. J. Signal Process. Image Process. Pattern Recognit. 9(4), 265–280 (2016)Google Scholar
  3. 3.
    Thounaojam, D.M., Khelchandra, T., Singh, KM., Roy, S.: A genetic algorithm and Fuzzy logic approach for video shot boundary detection. Comput. Intell. Neurosci., Vol. 2016, Article ID 8469428, 11 pagesGoogle Scholar
  4. 4.
    Moeglein, W.A., Griswold, R., Mehdi, B.L., Browning, N.D., Teuton, J.: Applying shot boundary detection for automated crystal growth analysis during in situ transmission electron microscope experiments. Adv. Struct. Chem. Imaging 3, 2 (2017)CrossRefGoogle Scholar
  5. 5.
    Tippaya, S., Khan, M.M., Chamnongthai, K.: Multi-modal visual features-based video shot boundary detection. IEEE Access 5, 12563–12575 (2017)CrossRefGoogle Scholar
  6. 6.
    Thounaojam, D. M., Trivedi, A., ManglemSingh, K., Roy, S.: A survey on video segmentation. In: Mohapatra, D.P., Patnaik, S., Mohapatra, D.P. (eds.) Intelligent Computing, Networking, and Informatics. Proceedings of the International Conference on Advanced Computing, Networking, and Informatics, India, June 2013, vol. 243 of Advances in Intelligent Systems and Computing, pp. 903–912, Springer, Berlin, Germany (2014)Google Scholar
  7. 7.
    Smeaton, A.F., Over, P., Doherty, A.R.: Video shot boundary detection: seven years of TRECVid activity. Comput. Vis. Image Underst. 114(4), 411–418 (2010)CrossRefGoogle Scholar
  8. 8.
    Wang, X., Wang, S., Chen, H.: A fast algorithm for MPEG video segmentation based on macroblock. In: Proceedings of the 4th International Conference on Fuzzy Systems and Knowledge Discovery (FSKD’07), vol. 2, pp. 715–718, Haikou, China, August (2007)Google Scholar
  9. 9.
    Abdulghafour, M.: Image segmentation using fuzzy logic and genetic algorithms. J. WSCG, 11 (1) (2003)Google Scholar
  10. 10.
    Pal, G., Rudrapaul, D., Acharjee, S., Ray, R., Chakraborty, S., Dey, N.: Video shot boundary detection: a review. In: Satapathy, S.C., Govardhan, A., Raju, K.S., Mandal, J.K. (eds.) Emerging ICT for Bridging the Future—Proceedings of the 49th Annual Convention of the Computer Society of India CSI Volume 2, vol. 338 of Advances in Intelligent Systems and Computing, 119–127. Springer (2015)Google Scholar
  11. 11.
    Küçüktunç, O., Güdükbay, U., Ulusoy, Ö.: Fuzzy color histogram-based video segmentation. Comput. Vis. Image Underst. 114(1), 125–134 (2010)CrossRefGoogle Scholar
  12. 12.
    Fang, H., Jiang, J., Feng, Y.: A fuzzy logic approach for detection of video shot boundaries. Pattern Recognit. 39(11), 2092–2100 (2006)CrossRefzbMATHGoogle Scholar
  13. 13.
    Sun, X., Zhao, L., Zhang, M.: A novel shot boundary detection method based on genetic algorithm-support vector machine. In: Proceedings of the 3rd International Conference on Intelligent Human-Machine Systems and Cybernetics (IHMS ’11), vol. 1, pp. 144–147, IEEE, Zhejiang, China, August (2011)Google Scholar
  14. 14.
    Chan, C., Wong, A.: Shot boundary detection using genetic algorithm optimization. In: Proceedings of the IEEE International Symposium on Multimedia (ISM ’11), pp. 327–332, IEEE, Dana Point, Calif, USA, December (2011)Google Scholar
  15. 15.
    Sun, W., Xu, G., Gong, P., Liang, S.: Fractal analysis of remotely sensed images: A review of methods and applications. Int. J. Remote Sens. 27(22), 4963–4990 (2006)CrossRefGoogle Scholar
  16. 16.
    Fernando, W.A.C., Canagrajah, C.N., Bull, D.R.: Scene change detection algorithms for content based video indexing and retrieval. Electron. Commun. Eng. J. 13, 117–126 (2001)CrossRefGoogle Scholar
  17. 17.
    Jiang, J., Weng, Y.: Video extraction for fast content access to MPEG compressed videos. IEEE Trans Circuits Syst. Video Technol. 14(5), 595–605 (2004)CrossRefGoogle Scholar
  18. 18.
    Fernando, W.A.C., Canagrajah, C.N., Bull, D.R.: A Unified approach to scene change detection in uncompressed and compresses video. IEEE Trans. Consum. Electron. 46(3), 769–779 (2000)CrossRefGoogle Scholar
  19. 19.
    Porter, S., Mirmehdi, M., Thosmas, B.: Temporal video segmentation and classification of edit effects. Image Vis. Comput. 21, 1098–1106 (2003)CrossRefGoogle Scholar
  20. 20.
    Fan, J., Zhou, S., Siddique, M.A.: Fuzzy color distribution chart -based shot boundary detection. Multimedia Tools Appl. 76, 10169–10190 (2017)CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC 2017

Authors and Affiliations

  1. 1.Faculty of Computer Science and Engineering Karunya UniversityCoimbatoreIndia
  2. 2.Faculty of Electrical and Electronic EngineeringCoimbatore Institute of TechnologyCoimbatoreIndia

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