Skip to main content
Log in

Application of a clustering algorithm in sports video image extraction and processing

  • Published:
The Journal of Supercomputing Aims and scope Submit manuscript

Abstract

In this paper, a motion video sequence extraction algorithm based on cumulative frame differential clustering is proposed for the same scene. Firstly, the difference results of the two frames are clustered, and then median filtering technology and morphological method are used to extract and process the clustered results for obtaining moving foreground objects. During the process of video object image extraction, the color space clustering algorithm and filtering algorithm are introduced to reduce color noise in image and improve the accuracy of target object location and extraction efficiency. Finally, the framework of extraction system are designed and implemented. The high performance and accuracy of the proposed method are proved by quantitative analysis and extraction results comparison. The research in this paper has theoretical and practical value for promoting the development and application of key frame extraction technology and content-based video retrieval. The experimental results show that the algorithm has better performance, in terms of low computational complexity, real-time performance and better segmentation results.

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.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8

Similar content being viewed by others

References

  1. Sun XD, Li AP, Li SD (2014) Research and implementation of micro-blog keyword extraction method based on clustering. Netinfo Secur 19(14):41–43

    Google Scholar 

  2. Wu Z, Xu P (2012) Research on the technology of video key frame extraction based on clustering. In: Fourth International Conference on Multimedia Information NETWORKING and Security 20(12):290–293

  3. Luo WQ, Wang CH, Zhao EP (2012) Research on clustering-based linear feature extraction algorithm simulation. J Xian Univ Arts Sci 15(9):12–16

    Google Scholar 

  4. Angadi S, Naik V (2014) Entropy based fuzzy C means clustering and key frame extraction for sports video summarization. In: Fifth International Conference on Signal and Image Processing 18(10):271–279

  5. Pan R, Tian Y, Wang Z (2011) Key-frame extraction based on clustering. In: IEEE International Conference on Progress in Informatics and Computing, 12(16):867–871

  6. Zhu DM, Wen X, Xia R (2012) Road extraction based on the algorithms of K-means clustering and hybrid model of SVM and FCM. Adv Mater Res 51(8):5738–5743

    Article  Google Scholar 

  7. Qin Y, Sun S, Ma X (2015) A background extraction and shadow removal algorithm based on clustering for ViBe. In: International Conference on Machine Learning and Cybernetics 18(15):52–57

  8. Joly A, Goëau H, Bonnet P et al (2014) Interactive plant identification based on social image data. Ecol Inform 23(9):22–34

    Article  Google Scholar 

  9. Wang J, Ji L, Liang A et al (2012) The identification of butterfly families using content-based image retrieval. Biosys Eng 111(1):24–32

    Article  Google Scholar 

  10. Jang SW, Park YJ, Kim GY et al (2011) An adult image identification system based on robust skin segmentation. J Imaging Sci Technol 55(2):020508

    Article  Google Scholar 

  11. Ghomsheh AN, Talebpour A (2012) A new skin detection approach for adult image identification. Res J Appl Sci Eng Technol 4(21):4535–4545

    Google Scholar 

  12. Mathieu F, Hild F, Roux S (2013) Image-based identification procedure of a crack propagation law. Eng Fract Mech 103(4):48–59

    Article  Google Scholar 

  13. Jianming H, Qiang M, Qi W et al (2012) Traffic congestion identification based on image processing. IET Intel Transport Syst 6(2):153–160

    Article  Google Scholar 

  14. Wang Z, Li H, Ying Z et al (2016) Review of plant identification based on image processing. Arch Comput Methods Eng 24(3):1–18

    MathSciNet  Google Scholar 

  15. Berntsson F, Baravdish G (2014) Coefficient identification in PDEs applied to image inpainting. Appl Math Comput 242(Complete):227–235

    MathSciNet  MATH  Google Scholar 

  16. Li H, Luo W, Qiu X et al (2018) Identification of various image operations using residual-based features. IEEE Trans Circuits Syst Video Technol 28(1):31–45

    Article  Google Scholar 

  17. Atsuchi M, Tsuji A, Usumoto Y et al (2013) Assessment of some problematic factors in facial image identification using a 2D/3D superimposition technique. Leg Med 15(5):244–248

    Article  Google Scholar 

  18. Lv Y (2011) An improved image blind identification based on inconsistency in light source direction. J Supercomput 58(1):50–67

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Shifei Zhao.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Zhao, S. Application of a clustering algorithm in sports video image extraction and processing. J Supercomput 75, 6070–6084 (2019). https://doi.org/10.1007/s11227-019-02901-x

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11227-019-02901-x

Keywords

Navigation