Ontology-Assisted Object Detection: Towards the Automatic Learning with Internet

  • Francesco Setti
  • Dong-Seon Cheng
  • Sami Abduljalil Abdulhak
  • Roberta Ferrario
  • Marco Cristani
Part of the Lecture Notes in Computer Science book series (LNCS, volume 8157)


Automatic detection approaches depend essentially on the use of classifiers, that in turn are based on the learning of a given training set. The choice of the training data is crucial: even if this aspect is often neglected, the visual information contained in the training samples can make the difference in a detection/classification scenario. A good training set has to be sufficiently informative to capture the nature of the object under analysis, but at the same time has to be generic enough to avoid overfitting and to cope with new instances of the object of interest. In this paper we follow those approaches that pursue automatic learning from Internet data. We try to show how such training set can be made more appropriate by leveraging on semantic technologies, like lexical resources and ontologies, in the task of retrieving images from the Web through the use of a search engine. Experiments on several object classes of the CalTech101 dataset promote our idea, showing an average increment on the detection accuracy of about 8%.


object detection one-class SVM machine learning ontology semantic search 


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Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  • Francesco Setti
    • 1
  • Dong-Seon Cheng
    • 2
  • Sami Abduljalil Abdulhak
    • 3
  • Roberta Ferrario
    • 1
  • Marco Cristani
    • 3
    • 4
  1. 1.ISTC–CNRTrentoItaly
  2. 2.Hankuk University of Foreign StudiesYonginCorea
  3. 3.Università degli Studi di VeronaVeronaItaly
  4. 4.Istituto Italiano di Tecnologia (IIT)GenovaItaly

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