A Hybrid Approach for Image Retrieval with Ontological Content-Based Indexing

  • Oleg Starostenko
  • Alberto Chávez-Aragón
  • J. Alfredo Sánchez
  • Yulia Ostróvskaya
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3773)


This paper presents a novel approach for image retrieval from digital collections. Specifically, we describe IRONS (Image Retrieval with Ontological Descriptions of Shapes), a system based on the application of several novel algorithms that combine low-level image analysis techniques with automatic shape extraction and indexing. In order to speed up preprocessing, we have proposed and implemented the convex regions algorithm and discrete curve evolution approach. The image indexing module of IRONS is addressed using two proposed algorithms: the tangent space and the two-segment turning function for shapes representation invariant to rotation and scale. Another goal of the proposed method is the integration of user-oriented descriptions, which leads to more complete retrieval by accelerating the convergence to the expected result. For the definition of image semantics, ontology annotation of sub-regions has been used.


Feature Vector Tangent Space Image Retrieval Resource Description Framework Indexing Module 
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.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Oleg Starostenko
    • 1
  • Alberto Chávez-Aragón
    • 1
  • J. Alfredo Sánchez
    • 1
  • Yulia Ostróvskaya
    • 1
  1. 1.Computer Science DepartmentUniversidad de las Américas, PueblaCholulaMexico

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