Unsupervised Classemes

  • Claudio Cusano
  • Riccardo Satta
  • Simone Santini
Part of the Lecture Notes in Computer Science book series (LNCS, volume 7585)


In this paper we present a new model of semantic features that, unlike previously presented methods, does not rely on the presence of a labeled training data base, as the creation of the feature extraction function is done in an unsupervised manner.

We test these features on an unsupervised classification (clustering) task, and show that they outperform primitive (low-level) features, and that have performance comparable to that of supervised semantic features, which are much more expensive to determine relying on the presence of a labeled training set to train the feature extraction function.


Feature Vector Semantic Feature Multiple Kernel Learning Sift Descriptor Primitive Feature 
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 2012

Authors and Affiliations

  • Claudio Cusano
    • 1
  • Riccardo Satta
    • 2
  • Simone Santini
    • 3
  1. 1.Department of Informatics, Systems and Communication (DISCo)Università degli Studi di Milano-BicoccaMilanoItaly
  2. 2.Department of Electrical and Electronic EngineeringUniversità di CagliariItaly
  3. 3.Escuela Politécnica SuperiorUniversidad Autónoma de MadridSpain

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