Set Similarity Measures for Images Based on Collective Knowledge

  • Valentina FranzoniEmail author
  • Clement H. C. Leung
  • Yuanxi Li
  • Paolo Mengoni
  • Alfredo Milani
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9155)


This work introduces a new class of group similarity where different measures are parameterized with respect to a basic similarity defined on the elements of the sets. Group similarity measures are of great interest for many application domains, since they can be used to evaluate similarity of objects in term of the similarity of the associated sets, for example in multimedia collaborative repositories where images, videos and other multimedia are annotated with meaningful tags whose semantics reflects the collective knowledge of a community of users. The group similarity classes are formally defined and their properties are described and discussed. Experimental results, obtained in the domain of images semantic similarity by using search engine based tag similarity, show the adequacy of the proposed approach in order to reflect the collective notion of semantic similarity.


Group similarity Semantic distance Image retrieval Data mining Collective knowledge Knowledge discovery 


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Copyright information

© Springer International Publishing Switzerland 2015

Authors and Affiliations

  • Valentina Franzoni
    • 1
    • 2
    Email author
  • Clement H. C. Leung
    • 3
  • Yuanxi Li
    • 3
  • Paolo Mengoni
    • 1
  • Alfredo Milani
    • 1
    • 3
  1. 1.Department of Mathematics and Computer ScienceUniversity of PerugiaPerugiaItaly
  2. 2.Department of Computer, Control and Mgmt. EngineeringUniversity of Rome La SapienzaRomeItaly
  3. 3.Department of Computer ScienceHong Kong Baptist UniversityKowloon TongHong Kong

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