Skip to main content
Log in

An efficient method for video shot boundary detection and keyframe extraction using SIFT-point distribution histogram

  • Regular Paper
  • Published:
International Journal of Multimedia Information Retrieval Aims and scope Submit manuscript

Abstract

In today’s digital era, there are large volumes of long-duration videos resulting from movies, documentaries, sports and surveillance cameras floating over internet and video databases (YouTube). Since manual processing of these videos are difficult, time-consuming and expensive, an automatic technique of abstracting these long-duration videos are very much desirable. In this backdrop, this paper presents a novel and efficient approach of video shot boundary detection and keyframe extraction, which subsequently leads to a summarized and compact video. The proposed method detects video shot boundaries by extracting the SIFT-point distribution histogram (SIFT-PDH) from the frames as a combination of local and global features. In the subsequent step, using the distance of SIFT-PDH of consecutive frames and an adaptive threshold video shot boundaries are detected. Further, the keyframes representing the salient content of each segmented shot are extracted using entropy-based singular values measure. Thus, the summarized video is then generated by combining the extracted keyframes. The experimental results show that our method can efficiently detect shot boundaries under both abrupt and gradual transitions, and even under different levels of illumination, motion effects and camera operations (zoom in, zoom out and camera rotation). With the proposed method, the computational complexity is comparatively less and video summarization is very compact.

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

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16
Fig. 17
Fig. 18

Similar content being viewed by others

References

  1. Ajmal M, Husnain Ashraf M, Shakir M, Abbas Y, Shah FA (2012) Video summarization: techniques and classification. In: Computer vision and graphics, lecture notes in computer science, vol 13, no 1, pp 7594

  2. Koprinska I, Carrato S (2001) Temporal video segmentation: a survey. Signal Process Image Commun Elsev 16(5):477–500

    Article  Google Scholar 

  3. Lowe DG (2004) Distinctive image features from scale-invariant keypoints. Int J Comput Vis 60(2):91–110

    Article  Google Scholar 

  4. Zhong Q, Lidan L, Tengfei G, Yongkun W (2013) An improved keyframe extraction method based on HSV colour space. J Softw 8(7):1751–1758

  5. Hong-cai F, Xiao-juan Y, Wei M, Cao Y (2010) A shot boundary detection method based on colour space. In: International conference on E-business and E-government, pp 1647–1650

  6. Janwe N, Bhoyar K (2013) Video shot boundary detection based on JND color histogram. In: Proceedings of the international conference on ICIIP, Shimla, pp 476–480

  7. Gunal ES, Canbek S, Adar N (2009) Gradual shot change detection in soccer videos via fractals. In: International conference on electrical and electronics engineering, pp 88–92

  8. Zhang H, Hu R, Song L (2011) A shot boundary detection method based on color feature. In: International conference on computer science and network technology (ICCSNT), vol 4, pp 2541–2544

  9. Lu Z, Shi Y (2013) Fast video shot boundary detection based on SVD and pattern matching. IEEE Trans Image Process 22(12):5136–5145

    Article  MathSciNet  Google Scholar 

  10. Wenzhu X, Lihong X (2010) A novel shot detection algorithm based on clustering. In: 2nd international conference on education technology and computer, vol 1, pp 570–572

  11. Huang CR, Lee HP, Chen CS (2008) Shot change detection via local keypoint matching. IEEE Trans Multimed 10(6):1097–1108

    Article  Google Scholar 

  12. Choudhury A, Medioni G (2012) A framework for robust online video contrast enhancement using modularity optimization. IEEE Trans Circ Syst Video Technol 22(9):1266–1279

    Article  Google Scholar 

  13. Sabbar W, Chergui A, Bekkhoucha A (2012) Video summarization using shot segmentation and local motion estimation. In: Second international conference on innovative computing technology (INTECH), pp 18–20

  14. Sujatha C, Mudenagudi U (2011) A study on keyframe extraction methods for video summary. In: International conference on computational intelligence and communication networks (CICN), vol 73, no 77, pp 7–9

  15. Gianluigi C, Raimondo S (2006) An innovative algorithm for key frame extraction in video summarization. J Real-Time Image Process 1:69–-88

    Article  Google Scholar 

  16. Azeroual A, Afdel K, El Hajji M, Douzi H (2014) Video shot detection and key-frames extraction using Faber Shauder DWT and SVD. Int J Comput Control Quant Inf Eng 8(12):2003–2006

  17. Lowe DG (1999) Object recognition from local scale-invariant features. In: International conference on computer vision, pp 1150–1157

  18. Brown M, Lowe D (2002) Invariant features from interest point groups. In: Proceedings of the british machine conference, pp 23.1–23.10

  19. Klema V, Laub AJ (1980) The singular value decomposition: its computation and some applications. IEEE Trans Autom Control 25(2):164–176

    Article  MathSciNet  MATH  Google Scholar 

  20. Shannon CE (1948) A mathematical theory of communication. Bell Syst Techn J 27:379–423

  21. Interaction Design Laboratory, The Open Video Project. http://www.open-video.org/

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Rachida Hannane.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Hannane, R., Elboushaki, A., Afdel, K. et al. An efficient method for video shot boundary detection and keyframe extraction using SIFT-point distribution histogram. Int J Multimed Info Retr 5, 89–104 (2016). https://doi.org/10.1007/s13735-016-0095-6

Download citation

  • Received:

  • Revised:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s13735-016-0095-6

Keywords

Navigation