Automatic Generation of Funny Cartoons Diary for Everyday Mobile Life

  • Injee Song
  • Myung-Chul Jung
  • Sung-Bae Cho
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4304)


The notable developments in pervasive and wireless technology enable us to collect enormous sensor data from each individual. With context-aware technologies, these data can be summarized into context data which support each individual’s reflection process of one’s own memory and communication process between the individuals. To improve reflection and communication, this paper proposes an automatic cartoon generation method for fun. Cartoon is a suitable medium for the reflection and the communication of one’s own memory, especially for the emotional part. By considering the fun when generating cartoons, the advantage of the cartoon can be boosted. For the funnier cartoon, diversity and consistency are considered during the cartoon generation. For the automated generation of diverse and consistent cartoon, context data which represent the user’s behavioral and mental status are exploited. From these context information and predefined user profile, the similarity between context and cartoon image is calculated. The cartoon image with high similarity is selected to be merged into cartoon cuts. Selected cartoon cuts are arranged with the constraints for the consistency of cartoon story. To evaluate the diversity and consistency of the proposed method, several operational examples are employed.


Semantic Similarity User Profile Background Image Context Data Consistency Constraint 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Dey, A.K., Abowd, G.D., Salber, D.: A conceptual framework and a toolkit for supporting the rapid prototyping of context-aware applications. Human-Computer Interaction 16(2), 97–166 (2001)CrossRefGoogle Scholar
  2. 2.
    Jain, R.: Multimedia electronic chronicles. IEEE Multimedia 10(3), 111–112 (2003)Google Scholar
  3. 3.
    Koster, R., Wright, W.: A Theory of Fun for Game Design. Paraglyph Press (2004)Google Scholar
  4. 4.
    Miikkulainen, R.: Script recognition with hierarchical feature maps. Connection Science 2, 83–101 (1990)CrossRefGoogle Scholar
  5. 5.
    Horvitz, E., Dumais, S., Koch, P.: Learning predictive models of memory landmarks. In: CogSci 2004: 26th Annual Meeting of the Cognitive Science Society (2004)Google Scholar
  6. 6.
    Viégas, F.B., Boyd, D., Nguyen, D.H., Potter, J., Donath, J.: Digital artifacts for remembering and storytelling: Post history and social network fragments. In: Proc. of the 37th Hawaii International Conference on System Sciences (2004)Google Scholar
  7. 7.
    Sumi, Y., Sakamoto, R., Nakao, K., Mase, K.: ComicDiary: Representing individual experience in a comic style. In: Borriello, G., Holmquist, L.E. (eds.) UbiComp 2002. LNCS, vol. 2498, pp. 16–32. Springer, Heidelberg (2002)CrossRefGoogle Scholar
  8. 8.
    Smeulders, A.W.M., Gupta, A.: Content-based image retrieval at the end of the early years. IEEE Trans. on Pattern Analysis and Machine Intelligence 22(12) (2000)Google Scholar
  9. 9.
    Inoue, M., Ueda, N.: Retrieving lightly annotated images using image similarities. In: Proc. of the 2005 ACM Symposium on Applied Computing, pp. 1031–1037 (2005)Google Scholar
  10. 10.
    Cardoso, J., Sheth, A.: Semantic e-workflow composition. Journal of Intelligent Information Systems 21(3), 191–225 (2003)CrossRefGoogle Scholar
  11. 11.
    Liu, H., Singh, P.: ConceptNet: A practical commonsense reasoning tool-kit. BT Technology Journal 22(4), 211–226 (2004)CrossRefGoogle Scholar
  12. 12.
    Singh, P., Williams, W.: LifeNet: A propositional model of ordinary human activity. In: Distributed and Collaborative Knowledge Capture Workshop (2003)Google Scholar
  13. 13.
    Barnard, K., Duygulu, P., Forsyth, D., de Freitas, N., Blei, D.M., Jordan, M.I.: Matching words and pictures. Journal of Machine Learning Research 3, 1107–1135 (2003)MATHCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • Injee Song
    • 1
  • Myung-Chul Jung
    • 1
  • Sung-Bae Cho
    • 1
  1. 1.Computer Science DepartmentYonsei UniversitySeoulSouth Korea

Personalised recommendations