Skip to main content
SpringerLink
Log in

Keyframe Extraction Using Sobel Fuzzified Weighted Approach

  • Conference paper
  • First Online:
Intelligent Systems Design and Applications (ISDA 2020)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1351))

  • 2100 Accesses

Abstract

Nowadays, progress in technology and the application of internet has led to exponential growth of video data. This drastic increase stipulates efficient video analysis techniques. Keyframe extraction is one of the technique that provides a succinct representation of video and are useful in various applications like video indexing and retrieval. In this direction, an efficient approach for keyframe extraction is proposed. The process begins by converting gray scale images of shots into gradient magnitude images using Sobel operator and establishing fuzzification. Further, 3*3 mask of sliding window is utilized in both overlapping and non-overlapping fashion to obtain Binary Weighted Codes (BWC) on a fuzzified edge image. In the subsequent step, feature set is obtained by applying entropy measure on BWC values of every frame within a shot. Finally, frame having highest entropy value is chosen as a keyframe of the corresponding shot. To verify the effectiveness of the proposed approach experiments were conducted on Open Video Project dataset. The experimental results shows that the proposed method outperforms some of the state-of-the-art algorithms with average of 93% fidelity measure.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 169.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD 219.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Asha Paul, M.K., Kavitha, J., Jansi Rani, P.A.: Key-frame extraction techniques: a review. Recent Pat. Comput. Sci. 11(1), 3–16 (2018)

    Article  Google Scholar 

  2. Money, A.G., Agius, H.: Video summarisation: a conceptual framework and survey of the state of the art. J. Vis. Commun. Image Representation 19(2), 121–143 (2008)

    Article  Google Scholar 

  3. De Avila, S.E.F., Lopes, A.P.B., Da Luz, A., De Albuquerque Araújo, A.: VSUMM: a mechanism designed to produce static video summaries and a novel evaluation method. Pattern Recognit. Lett. 32(1), 56–68 (2011)

    Google Scholar 

  4. Hanjalic, A., Zhang, H.: An integrated scheme for automated video abstraction based on unsupervised cluster-validity analysis. IEEE Trans. Circuits Syst. Video Technol. 9(8), 1280–1289 (1999)

    Article  Google Scholar 

  5. Truong, B.T., Venkatesh, S.: Video abstraction: a systematic review and classification. ACM Trans. Multimedia Comput. Commun. Appl. (TOMM) 3(1), 3 (2007)

    Google Scholar 

  6. Jadhav, P.S., Jadhav, D.S.: Video summarization using higher order color moments (VSUHCM). In: Procedia Computer Science. Elsevier B.V., pp. 275–281 (2015)

    Google Scholar 

  7. Ejaz, N., Tariq, T.B., Baik, S.W.: Adaptive key frame extraction for video summarization using an aggregation mechanism. J. Vis. Commun. Image Representation 23(7), 1031–1040 (2012)

    Article  Google Scholar 

  8. Furini, M., Geraci, F., Montangero, M., Pellegrini, M.: STIMO: STIll and MOving video storyboard for the web scenario. Multimedia Tools Appl. 46(1), 47–69 (2010)

    Article  Google Scholar 

  9. Abdesselam, A.: Improving local binary patterns techniques by using edge information. Lect. Notes Softw. Eng. 1(4), 360–363 (2013)

    Article  Google Scholar 

  10. Nandini, H.M., Chethan, H.K., Rashmi, B.S.: Shot based keyframe extraction using edge-LBP approach. Journal of King Saud University - Computer and Information Sciences (2020)

    Google Scholar 

  11. Lopez-Molina, C., De Baets, B., Bustince, H.: Generating fuzzy edge images from gradient magnitudes. Comput. Vis. Image Underst. 115(11), 1571–1580 (2011)

    Article  Google Scholar 

  12. Rashmi, B.S., Nagendraswamy, H.S.: Video shot boundary detection using block based cumulative approach. Multimedia Tools and Applications, pp. 1–24 (2020)

    Google Scholar 

  13. Mashtalir, S., Mikhnova, O.: Key frame extraction from video: framework and advances. Int. J. Comput. Vis. Image Process. (IJCVIP) 4(2), 68–79 (2014)

    Article  Google Scholar 

  14. Angadi, S., Naik, V.: Entropy based fuzzy C means clustering and key frame extraction for sports video summarization. In: Proceedings - 2014, ICSIP 2014, pp. 271–279. IEEE Computer Society (2014)

    Google Scholar 

  15. Chen, M., Han, X., Zhang, H., Lin, G., Kamruzzaman, M.M.: Quality-guided key frames selection from video stream based on object detection. J. Vis. Commun. Image Representation 65, 102678 (2019)

    Article  Google Scholar 

  16. Rashmi, B.S., Nagendraswamy, H.S.: Shot-based keyframe extraction using bitwise-XOR dissimilarity approach. In: In International Conference on Recent Trends in Image Processing and Pattern Recognition, pp. 305–316. Springer, Singapore (2016)

    Google Scholar 

  17. Hannane, R., Elboushaki, A., Afdel, K.: MSKVS: adaptive mean shift-based keyframe extraction for video summarization and a new objective verification approach. J. Vis. Commun. Image Representation 55, 179–200 (2018)

    Article  Google Scholar 

  18. Ujwalla, G., Hajari, K., Yogesh, G.: Deep learning approach to key frame detection in human action action videos. In: Recent Trends in Computational Intelligence, vol. 13. IntechOpen (2020)

    Google Scholar 

  19. Gharbi, H., Bahroun, S., Zagrouba, E.: Key frame extraction for video summarization using local description and repeatability graph clustering. Sign. Image Video Process. 13(3), 507–515 (2019)

    Article  Google Scholar 

  20. Hannane, R., Elboushaki, A., Afdel, K., Naghabhushan, P., Javed, M.: An efficient method for video shot boundary detection and keyframe extraction using SIFT-point distribution histogram. Int. J. Multimedia Inform. Retrieval 5(2), 89–104 (2016)

    Article  Google Scholar 

  21. Doulamis, A.D., Doulamis, N.D., Kollias, S.D.: Efficient video summarization based on a fuzzy video content representation. In: 2000 IEEE (ISCAS), vol. 4, pp. 301–304 (2000)

    Google Scholar 

  22. Pan, G., Zheng, Y., Zhang, R., Han, Z., Sun, D., Qu, X.: A bottom-up summarization algorithm for videos in the wild. EURASIP J. Adv. Sign. Process. 2019, 15 (2019)

    Google Scholar 

  23. Rashmi, B.S., Nagendraswamy, H.S.: Effective video shot boundary detection and keyframe selection using soft computing techniques. (IJCVIP) 8(2), 27–48 (2018)

    Google Scholar 

  24. Elahi, G.M., Yang, Y.H.: Online learnable keyframe extraction in videos and its application with semantic word vector in action recognition, vol. 12434 (2020). arXiv preprint arXiv:2009

    Google Scholar 

  25. Dhagdi, S.T., Deshmukh, P.R.: Keyframe based video summarization using automatic threshold & edge matching rate. Int. J. Sci. Res. Publ. 2(7), 1–12 (2012)

    Google Scholar 

  26. Canny, J.: A computational approach to edge detection. IEEE Trans. Pattern Anal. Mach. Intell. PAMI-8, 6, 679–698 (1986)

    Google Scholar 

  27. Sobel, I., Feldman, G.: An isotropic 3×3 image gradient operator. Stanford Artificial Intelligence Project, pp. 271–272 (1968)

    Google Scholar 

  28. Roberts, L.G.: Machine perception of three-dimensional solids. PhD diss., Massachusetts Institute of Technology (1963)

    Google Scholar 

  29. Lindeberg, T.: Edge detection and ridge detection with automatic scale selection. Int. J. Comput. Vis. 30(2), 117–156 (1998)

    Article  Google Scholar 

  30. Sun, G., Liu, Q.H., Liu, Q., Ji, C., Li, X.: A novel approach for edge detection based on the theory of universal gravity. Pattern Recogn. 40(10), 2766–2775 (2007)

    Article  Google Scholar 

  31. Rosin, P.L.: Unimodal thresholding. Pattern Recogn. 34(11), 2083–2096 (2001)

    Article  Google Scholar 

  32. Zadeh, L.A.: Fuzzy sets. Inform. Control 8(3), 338–353 (1965)

    Google Scholar 

  33. Kuo, Y.H., Lee, C.S., Liu, C.C.: New fuzzy edge detection method for image enhancement. In: Proceedings of 6th International Fuzzy Systems Conference, vol. 2, pp. 1069–1074. IEEE (1997)

    Google Scholar 

  34. Wang, C., Shen, H.W.: Information theory in scientific visualization. Entropy 13(1), 254–273 (2011)

    Article  Google Scholar 

  35. Chang, H.S., Sull, S., Lee, S.U., Member, S.: Efficient video indexing scheme for content-based retrieval. IEEE Trans. Circ. Syst. Video Technol. 9, 1269–1279 (1999)

    Article  Google Scholar 

  36. Besiris, D., Fotopoulou, F., Laskaris, N., Economou, G.: Key frame extraction in video sequences: a vantage points approach. In: 2007 IEEE 9th International Workshopon Multimed Signal Process MMSP 2007, pp. 434–437. IEEE (2007)

    Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering, Maharaja Research Foundation, Maharaja Institute of Technology, Mysuru, India

    H. M. Nandini & H. K. Chethan

  2. Department of Information Technology, Karnataka State Open University, Mysuru, India

    H. M. Nandini & B. S. Rashmi

Authors
  1. H. M. Nandini
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. H. K. Chethan
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. B. S. Rashmi
    View author publications

    You can also search for this author in PubMed Google Scholar

Editor information

Editors and Affiliations

  1. Scientific Network for Innovation and Research Excellence, Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Ajith Abraham

  2. Department of Computer Science, Università degli Studi di Milano, Milan, Milano, Italy

    Vincenzo Piuri

  3. Machine Intelligence Research Labs (MIR Labs), Auburn, WA, USA

    Niketa Gandhi

  4. Campus Centre de Créteil, Université Paris-Est Créteil, Créteil, France

    Patrick Siarry

  5. Department of Construction Management and Real Estate, Vilnius Gediminas Technical University, Vilnius, Lithuania

    Arturas Kaklauskas

  6. School of Engineering, Instituto Superior de Engenharia do Porto, Porto, Portugal

    Ana Madureira

Rights and permissions

Reprints and permissions

Copyright information

© 2021 The Author(s), under exclusive license to Springer Nature Switzerland AG

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Nandini, H.M., Chethan, H.K., Rashmi, B.S. (2021). Keyframe Extraction Using Sobel Fuzzified Weighted Approach. In: Abraham, A., Piuri, V., Gandhi, N., Siarry, P., Kaklauskas, A., Madureira, A. (eds) Intelligent Systems Design and Applications. ISDA 2020. Advances in Intelligent Systems and Computing, vol 1351. Springer, Cham. https://doi.org/10.1007/978-3-030-71187-0_22

Download citation

Publish with us

Policies and ethics

Search

Navigation