Intelligent Collage System

  • Margarita Favorskaya
  • Elena Yaroslavtzeva
  • Konstantin Levtin
Conference paper
Part of the Smart Innovation, Systems and Technologies book series (SIST, volume 14)

Abstract

Automatic collage design is a widely spread task in many applications, from a design of home collages to commercial advertising projects. We have proposed an intelligent collage system based on adaptive segmentation of Regions of Interest (ROI) in photos or video frames. Under ROI we assume images of people, their faces, animals, cars, ships, buildings or other large objects which are basically situated in the center of photos. Also we enhanced a method of a seamless blending of collage regions using flexible contours and color distribution on region boundaries. In the case of video sequence, we use an adaptive selection of frames and a special algorithm for removing of non-informative or repeatable frames. The intelligent collage system can work in automatic or handle modes, in last case with tuning of parameters. The main advantage of our approach is a great variability of different possible placements of collage regions and a high aesthetic effect of a designed collage.

Keywords

Video Sequence Current Frame Collage Region Video Collage Shape Estimation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Diakopoulos, N., Essa, I.: Mediating Photo Collage Authoring. In: UIST, pp. 183–186 (2005)Google Scholar
  2. 2.
    Man, H.-L., Singhal, N., Cho, S., Park, I.K.: Mobile photo collage. In: IEEE WECV, pp. 24–30 (2010)Google Scholar
  3. 3.
    Mei, T., Hua, X.-S., Zhu, C.-Z., Zhou, H.-Q., Li, S.: Home Video Visual Quality Assessment with Spatiotemporal Factors. IEEE Transactions on Circuits and Systems for Video Technology 17(6), 699–706 (2007)CrossRefGoogle Scholar
  4. 4.
    Yeung, M.M., Yeo, B.L.: Video visualization for compact presentation and fast browsing of pictorial content. IEEE Trans. on CSVT 7(5), 771–785 (1997)Google Scholar
  5. 5.
    Rother, C., Bordeaux, L., Hamadi, Y., Blake, A.: Autocollage. In: SIGGRAGPH, pp. 847–852 (2006)Google Scholar
  6. 6.
    Wang, T., Mei, T., Hua, X.-S., Liu, X., Zhou, H.-Q.: Video Collage: A Novel Presentation of Video Sequence. In: ICME, pp. 1479–1482 (2007)Google Scholar
  7. 7.
    Yang, B., Mei, T., Sun, L.-F., Yang, S.-Q., Hua, X.-S.: Free-Shaped Video Collage. In: Satoh, S., Nack, F., Etoh, M. (eds.) MMM 2008. LNCS, vol. 4903, pp. 175–185. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  8. 8.
    Chiu, P., Girgensohn, A., Liu, Q.: Stained-glass visualization for highly condensed video summaries. In: ICME, pp. 2059–2062 (2004)Google Scholar
  9. 9.
    Ma, Y.-F., Zhang, H.-J.: Video snapshot: A bird view of video sequence. In: MMM 2005, pp. 94–101 (2005)Google Scholar
  10. 10.
    Favorskaya, M.: A Way to Recognize Dynamic Visual Images on the Basis of Group Transformations. Pattern Recognition and Image Analysis 21(2), 179–183 (2011)CrossRefGoogle Scholar
  11. 11.
    Favorskaya, M.: Motion Estimation for Object Analysis and Detection in Videos. In: Handbook “Advances in Reasoning-Based Image Processing, Analysis and Intelligent Systems: Conventional and Intelligent Paradigms, pp. 211–253. Springer (2012)Google Scholar
  12. 12.
    Favorskaya, M., Zotin, A., Damov, M.: Intelligent Inpainting System for Texture Reconstruction in Videos with Text Removal. In: ICUMT, pp. 867–874 (2010)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Margarita Favorskaya
    • 1
  • Elena Yaroslavtzeva
    • 1
  • Konstantin Levtin
    • 1
  1. 1.Siberian State Aerospace UniversityKrasnoyarskRussia

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