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Selecting Suitable Image Retargeting Methods with Multi-instance Multi-label Learning

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Brain and Health Informatics (BHI 2013)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 8211))

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Abstract

Althogh the diversity of mobile devices brings in image retargeting technique to effectively display images on various screens, no existing image retargeting method can handle all images well. In this paper, we propose a novel approach to select suitable image retargeting methods solely based on original image characteristic, which can obtain acceptable selection accuracy with low computation cost. First, the original image is manually annotated with several simple features. Then, suitable methods are automatically selected from candidate image retargeting methods using multi-instance multi-label learning. Finally, target images are generated by the selected methods. Experiments demonstrate the effectiveness of the proposed approach.

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References

  1. Smith, T.F., Waterman, M.S.: Identification of Common Molecular Subsequences. J. Mol. Biol. 147, 195–197 (1981)

    Article  Google Scholar 

  2. May, P., Ehrlich, H.C., Steinke, T.: ZIB Structure Prediction Pipeline: Composing a Complex Biological Workflow through Web Services. In: Nagel, W.E., Walter, W.V., Lehner, W. (eds.) Euro-Par 2006. LNCS, vol. 4128, pp. 1148–1158. Springer, Heidelberg (2006)

    Chapter  Google Scholar 

  3. Foster, I., Kesselman, C.: The Grid: Blueprint for a New Computing Infrastructure. Morgan Kaufmann, San Francisco (1999)

    Google Scholar 

  4. Czajkowski, K., Fitzgerald, S., Foster, I., Kesselman, C.: Grid Information Services for Distributed Resource Sharing. In: 10th IEEE International Symposium on High Performance Distributed Computing, pp. 181–184. IEEE Press, New York (2001)

    Chapter  Google Scholar 

  5. Foster, I., Kesselman, C., Nick, J., Tuecke, S.: The Physiology of the Grid: an Open Grid Services Architecture for Distributed Systems Integration. Technical report, Global Grid Forum (2002)

    Google Scholar 

  6. National Center for Biotechnology Information, http://www.ncbi.nlm.nih.gov

  7. Cho, S., Choi, H., Matsushita, Y., Lee, S.: Image Retargeting Using Importance Diffusion. In: IEEE International Conference on Image Processing, Cairo (2009)

    Google Scholar 

  8. Rubinstein, M., Shamir, A., Avidan, S.: Improved Seam Carving for Video Retargeting. In: ACM International Conference on Computer Graphics and Interactive Techniques, New York (2008)

    Google Scholar 

  9. Wolf, L., Guttmann, M., Cohen-Or, D.: Non-homogeneous Content-driven video-retargeting. In: IEEE International Conference on Computer Vision, Rio de Janeiro (2007)

    Google Scholar 

  10. Wang, Y.S., Tai, C.L., Sorkine, O., Lee, T.Y.: Optimized Scale-and-stretch for Image Resizing. In: ACM International Conference on Computer Graphics and Interactive Techniques in Asia, Singapore (2008)

    Google Scholar 

  11. Rubinstein, M., Shamir, A., Avidan, S.: Multi-operator Media Retargeting. In: ACM International Conference on Computer Graphics and Interactive Techniques, New Orleans (2009)

    Google Scholar 

  12. Pritch, Y., Kav-Venaki, E., Peleg, S.: Shift-map Image Editing. In: IEEE International Conference on Computer Vision, Kyoto (2009)

    Google Scholar 

  13. Krähenbühl, P., Lang, M., Hornung, A., Gross, M.: A System for Retargeting of Streaming Video. In: ACM International Conference on Computer Graphics and Interactive Techniques in Asia, Yokohama (2009)

    Google Scholar 

  14. Rubinstein, M., Gutierrez, D., Sorkine, O., Shamir, A.: A Comparative Study of Image Retargeting. In: ACM International Conference on Computer Graphics and Interactive Techniques in Asia, Seoul (2010)

    Google Scholar 

  15. Zhou, Z.H., Zhang, M.L., Huang, S.J., Li, Y.F.: Multi-instance Multi-label Learning. Artificial Intelligence 176(1), 2291–2320 (2012)

    Article  MathSciNet  MATH  Google Scholar 

  16. Edgar, G.A.: Measure, Topology, and Fractal Geometry. Springer, Berlin (1990)

    Google Scholar 

  17. Boutell, M.R., Luo, J., Shen, X., Brown, C.M.: Learning Multi-label Scene Classification. Pattern Recognition 37(9), 1757–1771 (2004)

    Article  Google Scholar 

  18. RetargetMe Dataset, http://people.csail.mit.edu/mrub/retargetme/

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Author information

Authors and Affiliations

  1. Software Institute, Nanjing University, China

    Muyang Song, Tongwei Ren, Jia Bei & Zhihong Zhao

  2. State Key Laboratory for Novel Software Technology, Nanjing University, China

    Tongwei Ren, Jia Bei & Zhihong Zhao

  3. Department of Computing, The Hong Kong Polytechnic University, China

    Yan Liu

Authors
  1. Muyang Song
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  2. Tongwei Ren
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  3. Yan Liu
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  4. Jia Bei
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  5. Zhihong Zhao
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Editor information

Editors and Affiliations

  1. Department of Systems Life Engineering, Maebashi Institute of Technology, 460-1 Kamisadori-cho, 371-0816, Maebashi, Gunma, Japan

    Kazayuki Imamura

  2. Electronics-Inspired Interdisciplinary Research Institute, Toyohashi University of Technology, 1-1 Hibarigaoka Tenpaku-cho, 441-8580, Toyohashi, Aichi, Japan

    Shiro Usui

  3. Department of Neurobiology and Behavior, Gunma University Graduate School of Medicine, 39-22 Showamachi 3-chome, 371-8511, Maebashi, Gunma, Japan

    Tomoaki Shirao

  4. The Smith Kettlewell Eye Research Institute, 2318 Fillmore Street, 94115, San Francisco, CA, USA

    Takuji Kasamatsu

  5. University of Rostock, 18051, Rostock, Germany

    Lars Schwabe

  6. Department of Life Science and Informatics, Maebashi Institute of Technology, 460-1 Kamisadori-cho, 371-0816, Maebashi, Gunma, Japan

    Ning Zhong

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© 2013 Springer International Publishing Switzerland

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Song, M., Ren, T., Liu, Y., Bei, J., Zhao, Z. (2013). Selecting Suitable Image Retargeting Methods with Multi-instance Multi-label Learning. In: Imamura, K., Usui, S., Shirao, T., Kasamatsu, T., Schwabe, L., Zhong, N. (eds) Brain and Health Informatics. BHI 2013. Lecture Notes in Computer Science(), vol 8211. Springer, Cham. https://doi.org/10.1007/978-3-319-02753-1_42

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  • DOI: https://doi.org/10.1007/978-3-319-02753-1_42

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-02752-4

  • Online ISBN: 978-3-319-02753-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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