Image classification using cluster cooccurrence matrices of local relational features

  • Authors:
  • Lokesh Setia;Alexandra Teynor;Alaa Halawani;Hans Burkhardt

  • Affiliations:
  • Albert Ludwigs University Freiburg, Freiburg im Breisgau, Germany;Albert Ludwigs University Freiburg, Freiburg im Breisgau, Germany;Albert Ludwigs University Freiburg, Freiburg im Breisgau, Germany;Albert Ludwigs University Freiburg, Freiburg im Breisgau, Germany

  • Venue:
  • MIR '06 Proceedings of the 8th ACM international workshop on Multimedia information retrieval
  • Year:
  • 2006

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Abstract

Image classification systems have received a recent boost from methods using local features generated over interest points, delivering higher robustness against partial occlusion and cluttered backgrounds. We propose in this paper to use relational features calculated over multiple directions and scales around these interest points. Furthermore, a very important design issue is the choice of similarity measure to compare the bags of local feature vectors generated by each image, for which we propose a novel approach by computing image similarity using cluster co-occurrence matrices of local features. Excellent results are achieved for a widely used medical image classification task, and ideas to generalize to other tasks are discussed