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A Method of Graphics Composition Using Differential SVG Documents

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Knowledge-Based Intelligent Information and Engineering Systems (KES 2008)

Part of the book series: Lecture Notes in Computer Science ((LNAI,volume 5178))

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Abstract

This paper describes a novel method extracting editing differences between pairs of SVG graphics documents. These differences are extracted based on the analysis of the tree structure of SVG, and are generalized in order to abstract from the specifics of targets and document structure. The generalized differences can then be applied to other SVG graphics, resulting in new, heretofore unavailable graphics. We show the effectiveness of our method with experiments involving a variety of SVG documents.

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References

  1. Scalable Vector Graphics (SVG), http://www.w3.org/Graphics/SVG/

  2. Ito, K., Hasida, K.: Ontology mapping to promote making and understanding pictograms. DBSJ Letters (in Japanese) 5(2), 93–96 (2006)

    Google Scholar 

  3. Matsuda, M., Ito, K., Dürst, M.J., Hasida, K.: A Rule Extraction Method for Aarranging Pictogram Components. DBSJ Letters (in Japanese) 6(1), 165–168 (2007)

    Google Scholar 

  4. Ito, K., Matsuda, M., Dürst, M.J., Hasida, K.: SVG Pictograms with Natural Language Based and Semantic Information. In: Proceedings of the 5th International Conference on Scalable Vector Graphics (SVG Open 2007) (2007)

    Google Scholar 

  5. Anandan, P., Irani, M., Kumar, R., Bergen, J.: Video as an image data source: efficient representations and applications. In: Proceedings of the 1995 International Conference on Image Processing, p. 318 (1995)

    Google Scholar 

  6. Peters, L.: Change detection in xml trees: a survey. In: 3rd Twente Student Conference on IT (2005)

    Google Scholar 

  7. Microsoft XML Diff. and Patch 1.0, http://apps.gotdotnet.com/xmltools/xmldiff/

  8. XML Diff., http://schemas.microsoft.com/xmltools/2002/xmldiff

  9. Marsicoi, D., Cinque, L., Levialdi, S.: Indexing pictorial documents by techniques. Image and Vision Computing 15, 119–141 (1997)

    Article  Google Scholar 

  10. Flickner, M., et al.: Query by image and video content: the QBIC system. Computer 28(9), 23–32 (1995)

    Article  Google Scholar 

  11. Smith, J.R., Chang, S.-F.: VisualSEEk: A fully automated content-based image query system. ACM Multimedia, 87–98 (1996)

    Google Scholar 

  12. Hayashi, T., Onai, R., Abe, K.: Vector image segmentation for content-based vector image retrieval. In: CIT 2007: Proceedings of the 7th IEEE International Conference on Computer and Information Technology, pp. 695–700 (2007)

    Google Scholar 

  13. Kim, B., Yoon, J.P.: Similarity measurement for aggregation of spatial objects. In: SAC 2005: Proceedings of the 2005 ACM symposium on Applied computing, pp. 1213–1217. ACM, New York (2005)

    Chapter  Google Scholar 

  14. Kushima, K., Akama, H., Kon’ya, S., Yamamuro, M.: Exsight: Highly accurate object based image retrieval system enhanced by redundant object extraction. In: Lu, H., Zhou, A. (eds.) WAIM 2000. LNCS, vol. 1846, pp. 331–343. Springer, Heidelberg (2000)

    Chapter  Google Scholar 

  15. Ashley, J., Barber, R., Flickner, M., Hafner, J., Lee, D., Niblack, W., Petkovic, D.: Automatic and semiautomatic methods for image annotation and retrieval in query by image content (QBIC). In: Proc. Storage and Retrieval for Image and Video Database III, vol. 2420, pp. 24–35 (1995)

    Google Scholar 

  16. Mohan, A., Papageorgiou, C., Poggio, T.: Example-based object detection in images by components. IEEE Transactions on Pattern Analysis and Machine Intelligence 23(4), 349–361 (2001)

    Article  Google Scholar 

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Ignac Lovrek Robert J. Howlett Lakhmi C. Jain

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© 2008 Springer-Verlag Berlin Heidelberg

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Matsuda, M., Ito, K., Dürst, M.J., Hasida, K. (2008). A Method of Graphics Composition Using Differential SVG Documents. In: Lovrek, I., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2008. Lecture Notes in Computer Science(), vol 5178. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-85565-1_97

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  • DOI: https://doi.org/10.1007/978-3-540-85565-1_97

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-540-85564-4

  • Online ISBN: 978-3-540-85565-1

  • eBook Packages: Computer ScienceComputer Science (R0)

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