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Automatic comic page segmentation based on polygon detection

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Abstract

Comic page segmentation aims to automatically decompose scanned comic images into storyboards (frames), which is the key technique to produce digital comic documents that are suitable for reading on mobile devices. In this paper, we propose a novel method for comic page segmentation by finding the quadrilateral enclosing box of each storyboard. We first acquire the edge image of the input comic image, and then extract line segments with a heuristic line segment detection algorithm. We perform line clustering to further merge the overlapped line segments and remove the redundancy line segments. Finally, we perform another round of line clustering and post-processing to compose the obtained line segments into complete quadrilateral enclosing boxes of the storyboards. The proposed method is tested on 2,237 comic images from 12 different printed comic series, and the experimental results demonstrate that our method is effective for comic image segmentation and outperforms the existing methods.

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References

  1. Arai K, Tolle H (2010) Automatic e-comic content adaptation. Int J Ubiquit Comput 1(1):1–11

    Google Scholar 

  2. Ballard DH (1981) Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognit 13(2):111–122

    Article  MATH  Google Scholar 

  3. Burns JB, Hanson AR, Riseman EM (1986) Extracting straight lines. IEEE Trans Pattern Anal Mach Intell 8(4):425–455

    Article  Google Scholar 

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

    Article  Google Scholar 

  5. Chung KL, Lin ZW, Huang ST, Huang YH, Liao HYM (2010) New orientation-based elimination approach for accurate line-detection. Pattern Recognit 31:11–19

    Article  Google Scholar 

  6. Forsyth DA, Ponce J (2002) Computer vision: a modern approach, 1st edn. Prentice Hall 467–490

  7. Gioi RG, Jakubowicz J, Morel JM, Randall G (2010) LSD: a fast line segment detector with a false detection control. IEEE Trans Pattern Anal Mach Intell 32(4):722–732

    Article  Google Scholar 

  8. Grana C, Borghesani D, Cucchiara R (2010) Optimized block-based connected components labeling with decision trees. IEEE Trans Image Process 19(6):1596–1609

    Article  MathSciNet  Google Scholar 

  9. Ho CT, Chen LH (1996) A high-speed algorithm for line detection. Pattern Recognit Lett 17:467–473

    Article  Google Scholar 

  10. In Y, Oie T, Higuchi M, Kawasaki S et al (2010) Fast frame decomposition and sorting by contour tracing for mobile phone comic images. Proc. International Conference on Visualization, imaging and simulation (VIS), Wisconsin, 2010:23–28

  11. Ishii D, Watanabe H (2010) A study on frame position detection of digitized comic images. Workshop on Picture Coding and Image Processing (PCSJ), Nagoya, 2010:124–125

  12. Jain AK, Yu B (1998) Document representation and its application to page decomposition. IEEE Trans Pattern Anal Mach Intell 20(12):294–308

    Article  Google Scholar 

  13. Lo RC, Tsai WH (1995) Gray-scale Hough transform for thick line detection in gray-scale images. Pattern Recognit 28(5):647–661

    Article  Google Scholar 

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

    Article  Google Scholar 

  15. Tanaka T, Shoji K, Toyama F, Miyamichi J (2007) Layout analysis of tree-structured scene frames in comic images. Proc. International Joint Conferences on Artificial Intelligence (IJCAI), Hyderabad, January 2007: 2885–2890

  16. Theodoridis S, Koutroumbas K (2008) Pattern recognition, 4th edn. Academic Press 20–50

  17. Viola P, Jones MJ (2004) Robust real-time face detection. Int J Comput Vis 57(2):137–154

    Article  Google Scholar 

  18. Xi J, Hu J, Wu L (2002) Page segmentation of Chinese newspapers. Pattern Recognit 35(12):2695–2704

    Article  MATH  Google Scholar 

  19. Yamada M, Budiarto R, Endoo M, Miyazaki S (2004) Comic image decomposition for reading comics on cellular phones. IEICE Trans Inf Syst E87-D(6):1370–1376

    Google Scholar 

Download references

Acknowledgments

This work is supported by National Basic Research Program of China, also Named “973 Program” (No. 2010CB735908).

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Authors and Affiliations

  1. Institute of Computer Science & Technology, Peking University, Beijing, China

    Luyuan Li, Yongtao Wang, Zhi Tang & Liangcai Gao

  2. State Key Laboratory of Digital Publishing Technology, Beijing, China

    Zhi Tang

Authors
  1. Luyuan Li
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  2. Yongtao Wang
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  3. Zhi Tang
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  4. Liangcai Gao
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Corresponding authors

Correspondence to Yongtao Wang or Zhi Tang.

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Li, L., Wang, Y., Tang, Z. et al. Automatic comic page segmentation based on polygon detection. Multimed Tools Appl 69, 171–197 (2014). https://doi.org/10.1007/s11042-012-1241-7

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  • DOI: https://doi.org/10.1007/s11042-012-1241-7

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