Ontology-Based Automatic Image Annotation Exploiting Generalized Qualitative Spatial Semantics

  • Christos V. Smailis
  • Dimitris K. Iakovidis
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7297)


Ontologies provide a formal approach to knowledge representation suitable for digital content annotation. In the context of image annotation a variety of ontology-based tools has been proposed. Most of them enable manual annotation of the images with higher level concepts whereas many of them are capable of formally representing low-level features as well. However, they either consider specific, usually quantitative, representations of the low-level features, or spatial semantics limited to 2D/3D image spaces. In this paper we propose a novel ontology-based methodology for automatic image annotation that exploits generalized qualitative spatial relations between objects, given an image domain. To represent knowledge for the spatial arrangements, we have implemented an ontology that models spatial relations in multi-dimensional vector spaces. The application of the proposed methodology is demonstrated for automatic annotation of segmented objects in chest radiographs.


Spatial Relation Image Domain Annotation Tool Image Annotation Segmented Object 
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.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Saathoff, C., Schenk, S., Scherp, A.: Kat: the k-space annotation tool. In: International Conference on Semantic and Digital Media Technologies, Germany (2008)Google Scholar
  2. 2.
    Arndt, R., Troncy, R., Staab, S., Hardman, L., Vacura, M.: COMM: Designing a Well-Founded Multimedia Ontology for the Web. In: Aberer, K., Choi, K.-S., Noy, N., Allemang, D., Lee, K.-I., Nixon, L.J.B., Golbeck, J., Mika, P., Maynard, D., Mizoguchi, R., Schreiber, G., Cudré-Mauroux, P. (eds.) ASWC 2007 and ISWC 2007. LNCS, vol. 4825, pp. 30–43. Springer, Heidelberg (2007)CrossRefGoogle Scholar
  3. 3.
    Halaschek-Wiener, C., Golbeck, J., Schain, A., Grove, M., Parsia, B., Hendler, J.A.: PhotoStuff — An Image Annotation Tool for the Semantic Web. In: 4th International Semantic Web Conference Posters, Galway (2005)Google Scholar
  4. 4.
    Petridis, K., Anastasopoulos, D., Saathoff, C., Timmermann, N., Kompatsiaris, Y., Staab, S.: M-OntoMat-Annotizer: Image Annotation Linking Ontologies and Multimedia Low-Level Features. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds.) KES 2006. LNCS (LNAI), vol. 4253, pp. 633–640. Springer, Heidelberg (2006)CrossRefGoogle Scholar
  5. 5.
    Simou, N., Tzouvaras, V., Avrithis, Y., Stamou, G., Kollias, S.: A visual descriptor ontology for multimedia reasoning. In: Workshop on Image Analysis for Multimedia Interactive Services, Montreux (2005)Google Scholar
  6. 6.
    Dasiopoulou, S., Giannakidou, E., Litos, G., Malasioti, P., Kompatsiaris, Y.: A Survey of Semantic Image and Video Annotation Tools. In: Paliouras, G., Spyropoulos, C.D., Tsatsaronis, G. (eds.) Multimedia Information Extraction. LNCS, vol. 6050, pp. 196–239. Springer, Heidelberg (2011)CrossRefGoogle Scholar
  7. 7.
    Hudelot, C., Atif, J., Bloch, I.: Fuzzy Spatial Relation Ontology for Image Interpretation. Fuzzy Sets and Systems 159, 1929–1951 (2008)MathSciNetCrossRefGoogle Scholar
  8. 8.
    Iakovidis, D.K., Schober, D., Boeker, M., Schulz, S.: An Ontology of Image Representations for Medical Image Mining. In: 9th International Conference on Information Technology and Applications in Biomedicine, Larnaca (2009)Google Scholar
  9. 9.
    Iakovidis, D.K., Smailis, C.V.: Efficient Semantically-Aware Annotation of Images. In: International Conference of Imaging Systems and Tech., Penang, pp. 146–149 (2011)Google Scholar
  10. 10.
    Baader, F., Calvanese, D., McGuinness, D., Nardi, D., Patel-Schneider, P.: The Description Logic Handbook: Theory, Impl. and Appl. Cambridge University Press, Cambridge (2003)Google Scholar
  11. 11.
    Shiraishi, J., et al.: Development of a Digital Image Database for Chest Radiographs with and without a Lung Nodule: Receiver Operating Characteristic Analysis of Radiologists Detection of Pulmonary Nodules. Am. J. Roentgenol. 174, 71–74 (2000)Google Scholar
  12. 12.
    Van Ginneken, B., Stegmann, M.B., Loog, M.: Segmentation of Anatomical Structures in Chest Radiographs using Supervised Methods: a Comparative Study on a Public Database. Medical Image Analysis 10, 19–40 (2006)CrossRefGoogle Scholar
  13. 13.
    Golbreich, C., Zhang, S., Bodenreider, O.: The foundational model of anatomy in OWL: Experience and perspectives. Journal of Web Semantics, Web Semantics: Science, Services and Agents on the World Wide Web 4, 181–195 (2006)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2012

Authors and Affiliations

  • Christos V. Smailis
    • 1
  • Dimitris K. Iakovidis
    • 1
  1. 1.Dept. of Informatics & Computer TechnologyTechnol. Educational Institute of LamiaGreece

Personalised recommendations