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Cartoon synthesis using constrained spreading activation network

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Abstract

In this paper, we propose an approach to synthesize cartoons from the existing cartoon data by controlling the character’s path which is defined by the cartoonists in a background image. First, detailed pre-experiments are conducted in which different cartoon features are extracted and compared. During the pre-experiments, three features extracted from edge, motion and color are demonstrated effectively for evaluating cartoon similarity according to the quantitative analysis. The three features are then fused and a Cartoon Frame Relationship Network is constructed. Based on the graph, we propose a Constrained Spreading Activation Algorithm to select candidate frames which are visually similar to the current frame to generate the next frame. The cartoons are synthesized by choosing the most appropriate frame from the candidates in accordance with the path designed by the cartoonists. When the new cartoons are applied into the background image, our approach coordinates the cartoon character’s size according to the image’s perspective as well. The experiment results demonstrate that the combination of the three proposed features are effective in similarity evaluation, and the candidates selected by Constrained Spreading Activation Algorithm, are more similar to the current frame compared with other algorithms. The results also show that our approach can synthesize visually smooth cartoons from the existing cartoon library.

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References

  1. Berger H, Dittenbach M, Merkl D (2004) An adaptive information retrieval system based on associative networks. In: Proc of the Conf on Conceptual Modelling, Dunedin, January 2004, pp 27–36

  2. Castro CL, Braga AP (2008) Optimization of the area under the ROC curve. Brazilian Symposium on Neural Networks (SBRN ‘08), Salvador, Oct. 2008, pp 141–146

  3. Chen H, Zheng NN, Liang L, Li Y, Xu YQ, Shum HY (2002) Pic-Toon: A personalized image-based cartoon system. In: Proc. of the ACM Int’l Conf. on multimedia (ACM MM’02), Juan les Pins, December 2002, pp 171–178

  4. Christina J, Bodenheimer B (2006) Re-using traditional animation: methods for semi-automatic segmentation and inbetweening. In Proc. of the ACM SIGGRAPH/Eurographics symposium on Computer animation, Vienna, Sept. 2006, pp 223–232

  5. Correa WT, Jensen RJ, Thayer CE, Finkelstein A (1998) Texture mapping for cel animation. In: Proc. of the ACM Int’l Conf. on Computer Graphics and Interactive Techniques (SIGGRAPH’98), Orlando, July 1998, pp 435–446

  6. Crestani F (1997) Application of spreading activation techniques in information retrieval. J Artificial Intelligence Review 11(6):453–582

    Article  Google Scholar 

  7. Criminisi A, Reid I, Zisserman A (2000) Single view metrology. J Computer Vision 40(2):123–148

    Article  MATH  Google Scholar 

  8. Fekete JD, Bizouarn E, Cournarie E, Galas T, Taillefer F (1995) Tic-TacToon: a paperless system for professional 2D animation. In: Proc of the ACM Int’l Conf. on Computer Graphics and Interactive Techniques (SIGGRAPH’95), Los Angeles, August 1995, pp 79–90

  9. Flach PA, Wu SM (2005) Repairing concavities in ROC curves. In: Proc of the Int’l Joint Conf on Artificial Intelligence, Edinburgh, July 2005, pp 702–707

  10. Galvin B, McCane B, Novins K, Mason D, Mills S (1998) Recovering motion fields: an evaluation of eight optical flow algorithms. In: Proc. of the Conf. on British Machine Vision Conference, Southampton, September 1998, pp 195–204

  11. Gary K, Szabo B, Vijayan L, Chapman B, Radhakrishnan J, Sivaraman A (2007) JMaPSS: Spreading activation search for the semanticweb. In: Proc of IEEE Int’l Conf on Information Reuse and Integration (IRI’07), Las Vegas, Aug. 2007, pp 104–109

  12. Jain AK, Vailaya A (1996) Image retrieval using color and shape. J Pattern Recognition 29(8):1233–1244

    Article  Google Scholar 

  13. Johanssoon G (1973) Visual perception of biological motion and a model for its analysis. J Perception and Psychophysics 14(2):201–211

    Article  Google Scholar 

  14. Juan CD, Bodenheimer B (2004) Cartoon textures. In: Proc of the ACM Int’l Symp. on Computer Animation, Grenoble, August 2004, pp 267–276

  15. Kort A (2002) Computer aided inbetweening. In: Proc. of Int’l Conf. on Non-photorealistic Animation and Rendering, Annecy, June 2002, pp 125–132

  16. Kullback S (1968) Information theory and statistics. Dover, New York

    Google Scholar 

  17. Li Y, Yu F, Xu YQ, Chang E, Shum HY (2001) Speech-driven cartoon animation with emotions. In: Proc. of the ACM Int’l Conf. on multimedia (ACM MM’01), Ottawa, September 2001, pp 365–371

  18. Liang DW, Liu Y, Huang QM, Zhu GY, Jiang SQ, Zhang ZB, Gao W (2005) Video2Cartoon: Generating 3D cartoon from broadcast soccer video. In: Proc. of the ACM Int’l Conf. on multimedia (ACM MM’05), Singapore, November 2005, pp 217–218

  19. Liu R, Alton P, Efros AA, Hodgins JK (2005) A data-driven approach to quantifying natural human motion. ACM Trans Graphics 24(3):1090–1097

    Article  Google Scholar 

  20. Mahmoudi F, Shanbehzadeh J (2003) Image retrieval based on shape similarity by edge orientation autocorrelogram. J Pattern Recognition 36(8):1725–1736

    Article  Google Scholar 

  21. Marr D, Hildreth EC (1980) Theory of edge detection. J Royal Society 207:187–217

    Article  Google Scholar 

  22. McDonnell R, Newell F, O’Sullivan C (2007) Smooth movers: perceptually guided human motion simulation. In: Proc of the ACM Symp on Computer Animation, San Diego, August 2007, pp 259–269

  23. Petrovic L, Fujito B, Williams L, Finkelstein A (2000). Shadows for cel animation. In: Proc. of the ACM Int’l Conf. on Computer Graphics and Interactive Techniques (SIGGRAPH’00), New Orleans, July 2000, pp 511–516

  24. Rastegari M, Gheissari N (2008) Multi-scale cartoon motion capture and retargeting without shape matching. In: Proc of Computing: Techniques and Applications (DICTA ‘08) Canberra, Dec. 2008, pp 320–326

  25. Rubner Y, Tomasi C, Guibas LJ (2000) The earth mover’s distance as a metric for image retrieval. J Computer Vision 40(2):99–121

    Article  MATH  Google Scholar 

  26. Shrager J, Hogg T, Huberman BA (1987) Observation of phase transitions in spreading activation networks. J Sci 236:1092–1094

    Article  Google Scholar 

  27. Tenenbaum JB, Silva VD, Langford JC (2000) A global geometric framework for nonlinear dimensionality reduction. J Sci 290:2319–2323

    Article  Google Scholar 

  28. Verma A, Subramaniam LV, Rajput N, Neti C, Faruquie TA (2004) Animating expressive faces across languages. IEEE Trans Multimedia 6(6):791–800

    Article  Google Scholar 

  29. William VH, Fabian DF, Frank VR (2005) Uniting cartoon textures with computer assisted animation. In Proc of the international conference on Computer graphics and interactive techniques in Australasia and South East Asia, Dunedin, Nov. 2005, pp 245–253

  30. Yu J, Zhuang YT, Xiao J, Chen C (2008) Adaptive control in cartoon data reusing. In: Proc of the Int’l Conf. on Computer Animation and Social Agents, Hasselt, June 2007, pp 571–582

  31. Zhuang Y, Yang Y, Wu F (2008) Mining semantic correlation of heterogeneous multimedia data for cross-media retrieval. IEEE Trans Multimedia 10(2):221–229

    Article  Google Scholar 

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Authors and Affiliations

  1. School of Computer Engineering, Nanyang Technological University, Singapore, Singapore

    Jun Yu & Hock-Soon Seah

  2. College of Computer Science, Zhejiang University, Hangzhou, Zhejiang, People’s Republic of China

    Yueting Zhuang

Authors
  1. Jun Yu
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  2. Hock-Soon Seah
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  3. Yueting Zhuang
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Corresponding author

Correspondence to Jun Yu.

Additional information

This work has been supported by the National Research Foundation grant, which is administered by the Media Development Authority Interactive Digital Media Programme Office, MDA (IDMPO).

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Yu, J., Seah, HS. & Zhuang, Y. Cartoon synthesis using constrained spreading activation network. Multimed Tools Appl 51, 1147–1174 (2011). https://doi.org/10.1007/s11042-010-0477-3

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