Advertisement

Key Frame Extraction Using Rough Set Theory for Video Retrieval

  • G. S. Naveen KumarEmail author
  • V. S. K. Reddy
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 898)

Abstract

Key frame is a representative frame which contains the entire information of the shot. It is used for indexing, classification, analysis and retrieval of video. The existing algorithms generate relevant key frames but they also generate a few redundant key frames. Some of them are not able to represent the entire shot since relevant key frames are not extracted. We have proposed a more effective algorithm based on DC coefficients and Rough Sets to prevail over the rest. It extracts the most relevant key frames by eliminating the vagueness of the selection of key frames. It can be applied for compressed MPEG videos hence decompression is not required. The performance of this algorithm shows its effectiveness.

Keyword

MPEG DC coefficients Rough Set Theory Key frame extraction Content based video retrieval 

References

  1. 1.
    Hu, W., et al.: A survey on visual content-based video indexing and retrieval. IEEE Trans. Syst. Man Cybern. Part C (Applications and Reviews) 41(6), 797–819 (2011)Google Scholar
  2. 2.
    Mendi, E., Bayrak, C.: Shot boundary detection and key frame extraction using salient region detection and structural similarity. In: Proceedings of the 48th Annual Southeast Regional Conference, p. 66. ACM (2010)Google Scholar
  3. 3.
    Sun, Z., Jia, K., Chen, H.: Video key frame extraction based on spatial-temporal color distribution. In: International Conference on Intelligent Information Hiding and Multimedia Signal Processing, IIHMSP’08, pp. 196–199. IEEE (2008)Google Scholar
  4. 4.
    Ejaz, N., Mehmood, I., Baik, S.W.: Efficient visual attention based framework for extracting key frames from videos. Signal Process. Image Commun. 28(1), 34–44 (2013)Google Scholar
  5. 5.
    Hua, G., Chen, C.W.: Distributed video coding with zero motion skip and efficient DCT coefficient encoding. In: 2008 IEEE International Conference on Multimedia and Expo, pp. 777–780. IEEE (2008)Google Scholar
  6. 6.
    Wang, T., Yu, W., Chen, L.: An approach to video key-frame extraction based on rough set. In: International Conference on Multimedia and Ubiquitous Engineering, MUE’07, pp. 590–596. IEEE (2007)Google Scholar
  7. 7.
    Gianluigi, C., Raimondo, S.: An innovative algorithm for key frame extraction in video summarization. J. Real-Time Image Proc. 1(1), 69–88 (2006)CrossRefGoogle Scholar
  8. 8.
    Liu, G., Zhao, J.: Key frame extraction from MPEG video stream. In: 2010 Third International Symposium on Information Processing (ISIP), pp. 423–427. IEEE (2010)Google Scholar
  9. 9.
    Xu, J., Yuting, S., Liu, Q.: Detection of double MPEG-2 compression based on distributions of DCT coefficients. Int. J. Pattern Recognit. Artif. Intell. 27(01), 1354001 (2013)MathSciNetCrossRefGoogle Scholar
  10. 10.
    Uehara, T., Safavi-Naini, R., Ogunbona, P.: Recovering DC coefficients in block-based DCT. IEEE Trans. Image Process. 15(11), 3592–3596 (2006)CrossRefGoogle Scholar
  11. 11.
    Pawlak, Z.: Rough set theory and its applications to data analysis. Cybern. Syst. 29(7), 661–688 (1998)CrossRefGoogle Scholar
  12. 12.
    Shirahama, K., Matsuoka, Y., Uehara, K.: Event retrieval in video archives using rough set theory and partially supervised learning. Multimed. Tools Appl. 57(1), 145–173 (2012)CrossRefGoogle Scholar
  13. 13.
    Wu, Z., Xu, P.: Shot boundary detection in video retrieval. In: 2013 IEEE 4th International Conference on Electronics Information and Emergency Communication (ICEIEC), pp. 86–89. IEEE (2013)Google Scholar
  14. 14.
    Borth, D., Ulges, A., Schulze, C., Breuel, T.M.: Keyframe extraction for video tagging & summarization. Informatiktage 2008, 45–48 (2008)Google Scholar
  15. 15.
    Nutanong, S., Jacox, E.H., Samet, H.: An incremental Hausdorff distance calculation algorithm. Proc. VLDB Endow. 4(8), 506–517 (2011)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Malla Reddy College of Engineering and TechnologyHyderabadIndia

Personalised recommendations