Skip to main content
SpringerLink
Log in

An object segmentation method for the color slow-motion videos based on adjacent frames gradual change

  • Published:
Multimedia Tools and Applications Aims and scope Submit manuscript

Abstract

Along with the high-speed cameras are more and more widely applied, how to automatically segment the region of interest in the slow-motion video is a new issue. In this paper, a color slow-motion video segmentation method is proposed. The main strategy is based on region growing and pixel color difference. A rapid color similarity computing method is improved and applied for classifying different pixels. An algorithm based on four directions corrosion is proposed to automatically extract the seed points for the serialized video frames. Utilizing this method, the color frames of the slow-motion videos can be segmented in series automatically. Also, the multithreading mode of parallel computing is introduced in the entire segmentation process. This method is not complicated but automatic. The regions of interest in the slow-motion video frames can be segmented clearly. This novel method can provide support to the video related applications.

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

Access this article

Log in via an institution

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
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13
Fig. 14
Fig. 15
Fig. 16

Similar content being viewed by others

References

  1. Babaguchi N, Kawai Y, Yasugi Y, Kitahashi T (2000) Linking live and replay scenes in broadcasted sports video. International Workshop on Multimedia Information Retriecal (MIR'00), Los Angeles, pp 205–208

    Google Scholar 

  2. Bai X, Wang J, Simons D, Sapiro G (2009) Video snapcut: robust video object cutout using localized classifiers. ACM Trans Graph (SIGGRAPH 2009) 28(3):70:1–70:11

    Google Scholar 

  3. Boykov YY, Jolly M-P (2001) Interactive graph cuts for optimal boundary and region segmentation of objects in N–D images. Proc IEEE Int Conf Comput Vis, 105–112

  4. Chen S-C, Shyu M-L, Peeta S, Zhang C (2003) Learning-based spatio-temporal vehicle tracking and indexing for transportation multimedia database systems. IEEE Trans Intell Trans Syst 4(3):154–167

    Article  Google Scholar 

  5. Collins L, Kanade F, Duggins T, Tolliver E, Hasegawa (2000) A system for video surveillance and monitoring.VSAM Final Report, Technical report CMURI-TR-00-12, Robotics Institute, Carnegie Mellon University

  6. Comaniciu D, Meer P (2002) Mean shift: a robust approach toward feature space analysis. IEEE Trans Pattern Anal Mach Intell 24(5):603–619

    Article  Google Scholar 

  7. Cucchiara R, Grana C, Piccardi M, Prati A (2003) Detecting moving objects, ghosts, and shadows in video streams. IEEE Trans Pattern Anal Mach Intell 25(10):1337–1342

    Article  Google Scholar 

  8. Cyganek B (2008) Color image segmentation with support vector machines: applications to road signs detection. Int J Neural Syst 18(4):339–345

    Article  Google Scholar 

  9. Haifeng X, Younis AA, Kabuka MR (2004) Automatic moving object extraction for content-based applications. IEEE Trans Circ Syst Video Technol 14(6):796–812

    Article  Google Scholar 

  10. Han X, Li J, Li Y, Xu X (2004) An approach of color object searching for vision system of soccer robot. Proc IEEE Int Conf Robot Biomim, 535–539

  11. Haritaoglu I, Harwood D, Davis LS (2000) W4: real-time surveillance of people and their activities. IEEE Trans Pattern Anal Mach Intell 22(8):809–830

    Article  Google Scholar 

  12. Ikonomakis N, Plataniotis KN, Venetsanopoulos AN (2000) Color image segmentation for multimedia applications. J Int Robot Syst 28(1–2):5–20

    Article  Google Scholar 

  13. KaewTrakulPong P, Bowden R (2003) A real time adaptive visual surveillance system for tracking low resolution colour targets in dynamically chaning scenes. Image Vis Comput 21(10):913–929

    Article  Google Scholar 

  14. Kass M, Witkin A, Terzopoulos D (1988) Snakes: active contour models. Int J Comput Vis 1(4):321–331

    Article  Google Scholar 

  15. Levin A, Lischinski D, Weiss Y (2008) A closed-form solution to natural image matting. IEEE Trans Pattern Anal Mach Intell 30(2):228–242

    Article  Google Scholar 

  16. Li H, Gu H, Han Y, Yang J (2010) Object-oriented classification of high-resolution remote sensing imagery based on an improved colour structure code and a support vector machine. Int J Remote Sens 31(6):1453–1470

    Article  Google Scholar 

  17. Li Y, Sun J, Tang C-K, Shum H-Y (2004) Lazy snapping. ACM Trans Graph 23(3):303–308

    Article  Google Scholar 

  18. Minetto R, Spina TV, Falcão AX, Leite NJ, Papa JP, Stolfi J (2012) IFTrace: Video segmentation of deformable objects using the Image Foresting Transform. Comput Vis Image Underst 116:274–291

    Article  Google Scholar 

  19. Nguyen TNA, Cai J, Zhang J, Zheng J (2012) Robust interactive image segmentation using convex active contours. IEEE Trans Image Process 21(8):3734–3743

    Article  MathSciNet  Google Scholar 

  20. Osher S, Sethian JA (1988) Fronts propagating with curvature-dependent speed: algorithms based on Hamilton–Jacobi formulations. J Comput Phys 79:12–49

    Article  MATH  MathSciNet  Google Scholar 

  21. Pan H, Li B, Sezan MI (2002) Automatic detection of replay segments in broadcast sports programs by detection of logos in scene transitions. ICASSP'02, Orlando, Florida, 4:3385–3388

  22. Priese L, Sturm P (2003) Introduction to the color structure code and its implementation. Available online at https://www.uni-koblenz-landau.de/koblenz/fb4/icv/agpriese/research/ColorImgSeg/download/csc.pdf

  23. Priese L, Rehrmann V, Schian R, Lakmann R, Bilderkennen L (1993) Traffic sign recognition based on color image evaluation. Proc IEEE Intell Veh Symp '93, 95–100

  24. Rother C, Kolmogorov V, Blake A (2004) Grabcut: interactive foreground extraction using iterated graph cuts. ACM Trans Graph 23(3):309–314

    Article  Google Scholar 

  25. Saha PK, Udupa JK, Odhner D (2000) Scale-based fuzzy connected image segmentation: theory, algorithms, and validation. Comput Vis Image Underst 77(2):145–174

    Article  Google Scholar 

  26. Shapiro LG, Stockman GC (2001) Computer Vision, Prentice-Hall, New Jersey, ISBN 0-13-030796-3. pp. 279–325

  27. Tao W, Jin H, Zhang Y (2007) Color image segmentation based on mean shift and normalized cuts. IEEE Trans Syst Man Cybern 37(5):1382–1389

    Article  Google Scholar 

  28. Udupa JK, Saha PK (2003) Fuzzy connectedness and image segmentation. Proc IEEE 91(10):1649–1669

    Article  Google Scholar 

  29. Udupa JK, Samarasekera S (1996) Fuzzy connectedness and object definition: theory, algorithms, and applications in image segmentation. Graph Model Image Process 58(3):246–261

    Article  Google Scholar 

  30. von Wangenheim A, Bertoldi RF, Abdala DD, Richter MM, Priese L, Schmitt F (2008) Fast two-step segmentation of natural color scenes using hierarchical region-growing and a color-gradient network. J Braz Comput Soc 14(4):29–40

    Article  Google Scholar 

  31. Xu C, Prince JL (1998) Snakes, shapes, and gradient vector flow. IEEE Trans Image Process 7(3):359–369

    Article  MathSciNet  Google Scholar 

  32. Yasuda G, Bin G (2003) Movement control of two-wheeled mobile robots using visual information. Proceedings of IEEE International Conference on Robotics. Intell Syst Signal Process 1:576–581

    Google Scholar 

  33. Yu Z, Bajaj CL (2002) Normalized gradient vector diffusion and image segmentation. Proc 7th Eur Conf Comput Vis (ECCV'02), 517–530.

  34. Zhong F, Qin X, Peng Q, Meng X (2012) Discontinuity-aware video object cutout. ACM Trans Graph (SIGGRAPH Asia 2012) 31(6):175:1–175:10

    Google Scholar 

Download references

Acknowledgments

Our thanks are due to the Youku (http://www.youku.com/), YouTube (http://www.youtube.com) and Tudou (http://www.tudou.com) for freely providing the color slow-motion video data sets. This study is supported by the National Natural Science Foundation of China (No. 61300085, 61033012), the Scientific Research Fund of Liaoning Provincial Education Department of China (No. L2013012).

Author information

Authors and Affiliations

  1. National Demonstrative School of Software, Dalian University of Technology, Dalian, 116620, China

    Bin Liu, Haojie Li, Xianyong Jia, Hui Zhang, Rui Huang, Zhaoliang Liu & Xu Zhao

Authors
  1. Bin Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Haojie Li
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Xianyong Jia
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Hui Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Rui Huang
    View author publications

    You can also search for this author in PubMed Google Scholar

  6. Zhaoliang Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

  7. Xu Zhao
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Bin Liu.

Rights and permissions

Reprints and permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Liu, B., Li, H., Jia, X. et al. An object segmentation method for the color slow-motion videos based on adjacent frames gradual change. Multimed Tools Appl 74, 7285–7329 (2015). https://doi.org/10.1007/s11042-014-1981-7

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11042-014-1981-7

Keywords

Search

Navigation