Advertisement

Contour-Based Progressive Identification of Known Shapes in Images

  • Stefano Ferilli
  • Floriana Esposito
  • Domenico Grieco
  • Marenglen Biba
Part of the Communications in Computer and Information Science book series (CCIS, volume 385)

Abstract

Information Retrieval in digital libraries is at the same time a hard task and a crucial issue. While the primary type of information available in digital documents is usually text, images play a very important role because they pictorially describe concepts that are dealt with in the document. Unfortunately, the semantic gap separating such a visual content from the underlying meaning is very wide, and additionally image processing techniques are usually very demanding in computational resources. Hence, only recently the area of Content-Based Image Retrieval has gained more attention. In this paper we describe a new technique to identify known objects in a picture. It is based on shape contours, and works by progressive approximations to save computational resources and to improve preliminary shape extraction. Small (controlled) and more extensive experiments are illustrated, yielding interesting results.

Keywords

Shape Recognition Information Retrieval Document Processing Digital Libraries 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Brause, R., Arlt, B., Tratar, E.: Project semacode: A scale-invariant object recognition system for content-based queries in images databases. Technical Report 11/99 (FB20), Johann Wolfgang Goethe University, Computer Science Dept., Frankfurt/Main (1999)Google Scholar
  2. 2.
    Chen, Y., Li, J., Wang, J.Z.: Machine Learning and Statistical Modeling Approaches to Image Retrieval. Information Retrieval, vol. 14. Kluwer (2004)Google Scholar
  3. 3.
    Ferilli, S., Basile, T.M.A., Biba, M., Di Mauro, N., Esposito, F.: A general similarity framework for horn clause logic. Fundamenta Informaticae 90, 43–66 (2009)zbMATHMathSciNetGoogle Scholar
  4. 4.
    Ferilli, S., Basile, T.M.A., Esposito, F., Biba, M.: A contour-based progressive technique for shape recognition. In: Proceedings of the 11th International Conference on Document Analysis and Recognition (ICDAR 2011), vol. 1, pp. 723–727. IEEE Computer Society (2011)Google Scholar
  5. 5.
    Hogendoorn, H.: The state of the art in visual object recognition (2006)Google Scholar
  6. 6.
    Shu, X., Wu, X.-J.: A novel contour descriptor for 2d shape matching and its application to image retrieval. Image and Vision Computing 29(4), 286–294 (2011)CrossRefGoogle Scholar
  7. 7.
    Szeliski, R.: Computer Vision: Algorithms and Applications. Springer (2011)Google Scholar
  8. 8.
    Zhang, D., Lu, G.: A comparative study of curvature scale space and fourier descriptors. Journal of Visual Communication and Image Representation 14(1), 41–60 (2003)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  • Stefano Ferilli
    • 1
  • Floriana Esposito
    • 1
  • Domenico Grieco
    • 1
  • Marenglen Biba
    • 2
  1. 1.Dipartimento di Informatica, LACAM LaboratoryUniversità degli Studi di Bari “Aldo Moro”Italy
  2. 2.Computer Science DepartmentUniversity of New YorkTiranaAlbania

Personalised recommendations