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SVG-to-RDF Image Semantization

  • Khouloud Salameh
  • Joe Tekli
  • Richard Chbeir
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8821)

Abstract

The goal of this work is to provide an original (semi-automatic) annotation framework titled SVG-to-RDF whichconverts a collection of raw Scalable vector graphic (SVG) images into a searchable semantic-based RDF graph structure that encodes relevant features and contents. Using a dedicated knowledge base, SVG-to-RDF offers the user possible semantic annotations for each geometric object in the image, based on a combination of shape, color, and position similarity measures. Our method presents several advantages, namely i) achieving complete semantization of image content, ii) allowing semantic-based data search and processing using standard RDF technologies, iii) while being compliant with Web standards (i.e., SVG and RDF) in displaying images and annotation results in any standard Web browser, as well as iv) coping with different application domains. Our solution is of linear complexity in the size of the image and knowledge base structures used. Using our prototype SVG2RDF, several experiments have been conducted on a set of panoramic dental x-ray images to underline our approach’s effectiveness, and its applicability to different application domains.

Keywords

Vector images SVG RDF semantic graph semantic processing image annotation and retrieval visual features image feature similarity 

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References

  1. 1.
    Abu Doush, I., et al.: Multimodal Presentation of Two-Dimensional Charts: An Investigation Using Open Office XML and Microsoft Excel. ACM TACCESS 3(2), 1–50 (2012)CrossRefGoogle Scholar
  2. 2.
    Awada, Y., et al.: Towards Digital Image Accessibility for Blind Users via Vibrating Touch Screen: A Feasibility Test Protocol. In: Inter. Conf. on Signal Image Tech. & Internet Systems, SITIS (2012)Google Scholar
  3. 3.
    Bai, S., et al.: Revised Aggregation-tree Used in Metadata Extraction from SVG Images. In: DMIN 2006, pp. 325–328 (2006)Google Scholar
  4. 4.
    Datta, R., Joshi, D., Li, J., Wang, J.Z.: Image Retrieval: Ideas, Influences and Trends of the New Age. ACM Computer Surveys 40(2), 1–60 (2008)CrossRefGoogle Scholar
  5. 5.
    Faloutsos, C., et al.: Efficient and Effective Querying by Image Content. JIISJ 3(3:4), 231–262 (1994)Google Scholar
  6. 6.
    Hall, P., Dowling, G.: Approximate String Matching. Computing Survey 12(4), 381–402 (1980)MathSciNetCrossRefGoogle Scholar
  7. 7.
    Hayes, P.: RDF Semantics. W3C Recommendation (2004), http://www.w3.org/TR/rdf-mt/ (cited May 26, 2014)
  8. 8.
    Jiang, K., et al.: Information Retrieval through SVG-based Vector Images Using an Original Method. In: Proc. of IEEE Inter. Conference on e-Business Engineering (ICEBE 2007), pp. 183–188 (2007)Google Scholar
  9. 9.
    Kiani, M., et al.: Ontology-Based Negotiation of Dental Therapy Options. In: Joshi, Boley, Akerkar (eds.) Advances in Semantic Computing, vol. 2, pp. 52–78 (2010)Google Scholar
  10. 10.
    Kim, E., et al.: A Hierarchical SVG Image Abstraction Layer for Medical Imaging. In: Society of Photo-Optical Instrumentation Engineers (SPIE) Conference, vol. 7628, p. 7 (2010)Google Scholar
  11. 11.
    Li, D., et al.: Shape similarity computation for SVG. Int. J. Comp. Science and Eng. 6(1/2) (2011)Google Scholar
  12. 12.
    Lin, D.: An Information-Theoretic Definition of Similarity. In: Inter. ICML Conf., pp. 296–304 (1998)Google Scholar
  13. 13.
    Liu, Y., Zhang, D., Lu, G., Ma, W.-Y.: A Survey of Content-Based Image Retrieval with High-Level Semantics. Pattern Recognition 40(1), 262–282 (2006)CrossRefGoogle Scholar
  14. 14.
    Long, F., et al.: Fundamentals of Content-based Image Retrieval. MM IR Management (2003)Google Scholar
  15. 15.
    Manjunath, B.S.: Color and Texture Descriptors. IEEE CSVT Trans. 6, 703–715 (2001)Google Scholar
  16. 16.
    Mezaris, V., et al.: An Ontology Approach to Object-based Image Retrieval. In: Proceedings of the International Conference on Image Processing (ICIP), vol. 2, pp. 511–514.Google Scholar
  17. 17.
    Noah, S., Sabtu, S.: Binding Semantic to a Sketch Based Query Specification Tool. The International Arab Journal of Information Technology 6(2), 116 (2009)Google Scholar
  18. 18.
    Peng, Z.R., Zhang, C.: The roles of geography markup language (GML), scalable vector graphics (SVG), and Web feature service(WFS) specifications in the development of Internet geographic information systems (GIS). Journal of Geographic Systems, 95–116 (2004)Google Scholar
  19. 19.
    Pentland, A., Picard, R.W., Scaroff, S.: Photobook: Content-based Manipulation for Image Databases. International Journal of Computer Vision 18(3), 233–254 (1996)CrossRefGoogle Scholar
  20. 20.
    Prudhommeaux, E., Seaborne, A.: SPARQL Query Language for RDF. W3C Recommendation (2008), http://www.w3.org/TR/rdf-sparql-query/ (cited May 26, 2014)
  21. 21.
    Resnik, P.: Using Information Content to Evaluate Semantic Similarity in a Taxonomy. In: Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI), vol. 1, pp. 448–453 (1995)Google Scholar
  22. 22.
    Richardson, R., Smeaton, A.: Using WordNet in a Knowledge-based approach to information retrieval. In: Proceedings of the BCS-IRSG Colloquium on Information Retrieval, pp. 1–16 (1995)Google Scholar
  23. 23.
    Smeulders, A., et al.: Content-based Image Retrieval at the End of the Early Years. IEEE Trans. of Pattern Analysis and Machine Intelligence 22(12), 1349–1380 (2000)CrossRefGoogle Scholar
  24. 24.
    Stanchev, P.L., Green Jr., D., Dimitrov, B.: High Level Color Similarity Retrieval. Inter. Journal on Information Theory and Applications 10(3), 363–369 (2003)Google Scholar
  25. 25.
    Torjmen, M., et al.: XML Multimedia Retrieval: From Relevant Textual Information to Relevant Multimedia Fragments. INEX: Initiative for the Evaluation of XML Retrieval (2009)Google Scholar
  26. 26.
    Tsikrika, T., Serdyukov, P., Rode, H., Westerveld, T., Aly, R., Hiemstra, D., de Vries, A.P.: Structured document retrieval, multimedia retrieval, and entity ranking using pF/Tijah. In: Fuhr, N., Kamps, J., Lalmas, M., Trotman, A. (eds.) INEX 2007. LNCS, vol. 4862, pp. 306–320. Springer, Heidelberg (2008)CrossRefGoogle Scholar
  27. 27.
    Wang, S., et al.: IGroup: Presenting Web Image Search Results in Semantic Clusters. In: CHI 2007 (2007)Google Scholar
  28. 28.
    W3C, Scalable Vector Graphics (SVG), http://www.w3.org/Graphics/SVG/ (cited May 26, 2014)
  29. 29.
    Wu, Z., Palmer, M.: Verb Semantics and Lexical Selection. In: Proceedings of the 32nd Annual Meeting of the Associations of Computational Linguistics, pp. 133–138 (1994)Google Scholar
  30. 30.
    Foley, J., van Dam, A., Feiner, S., Hughes, J.: Computer Graphics: Principles and Practice. Addison Wesley, Reading (1990)Google Scholar
  31. 31.
    Alpaydin: Introduction to Machine Learning. MIT Press, Cambridge (2004)Google Scholar
  32. 32.
    Ehrig, M., Sure, Y.: Ontology Mapping - an Integrated Approach. In: Bussler, C.J., Davies, J., Fensel, D., Studer, R. (eds.) ESWS 2004. LNCS, vol. 3053, pp. 76–91. Springer, Heidelberg (2004)Google Scholar

Copyright information

© Springer International Publishing Switzerland 2014

Authors and Affiliations

  • Khouloud Salameh
    • 1
  • Joe Tekli
    • 2
  • Richard Chbeir
    • 1
  1. 1.LIUPPA LaboratoryUniversity of Pau and Adour Countries (UPPA)AngletFrance
  2. 2.School of EngineeringLebanese American University (LAU)Lebanon

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