Fusing MPEG-7 visual descriptors for image classification

  • Authors:

  • Evaggelos Spyrou;Hervé Le Borgne;Theofilos Mailis;Eddie Cooke;Yannis Avrithis;Noel O'Connor

  • Affiliations:

  • Image, Video and Multimedia Systems Laboratory, National Technical University of Athens, Athens, Greece;Center for Digital Video Processing, Dublin City University, Ireland;Image, Video and Multimedia Systems Laboratory, National Technical University of Athens, Athens, Greece;Center for Digital Video Processing, Dublin City University, Ireland;Image, Video and Multimedia Systems Laboratory, National Technical University of Athens, Athens, Greece;Center for Digital Video Processing, Dublin City University, Ireland

  • Venue:
  • ICANN'05 Proceedings of the 15th international conference on Artificial neural networks: formal models and their applications - Volume Part II
  • Year:
  • 2005

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Abstract

This paper proposes three content-based image classification techniques based on fusing various low-level MPEG-7 visual descriptors. Fusion is necessary as descriptors would be otherwise incompatible and inappropriate to directly include e.g. in a Euclidean distance. Three approaches are described: A "merging" fusion combined with an SVM classifier, a back-propagation fusion combined with a KNN classifier and a Fuzzy-ART neurofuzzy network. In the latter case, fuzzy rules can be extracted in an effort to bridge the "semantic gap" between the low-level descriptors and the high-level semantics of an image. All networks were evaluated using content from the repository of the aceMedia project1 and more specifically in a beach/urban scene classification problem.