Improving the accuracy of global feature fusion based image categorisation

Authors:
Ville Viitaniemi;Jorma Laaksonen
Affiliations:
Laboratory of Computer and Information Science, Helsinki University of Technology, Finland;Laboratory of Computer and Information Science, Helsinki University of Technology, Finland
Venue:
SAMT'07 Proceedings of the semantic and digital media technologies 2nd international conference on Semantic Multimedia
Year:
2007

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Abstract

In this paper we consider the task of categorising images of the Corel collection into semantic classes. In our earlier work, we demonstrated that state-of-the-art accuracy of supervised categorising of these images could be improved significantly by fusion of a large number of global image features. In this work, we preserve the general framework, but improve the components of the system: we modify the set of image features to include interest point histogram features, perform elementary feature classification with support vector machines (SVM) instead of self-organising map (SOM) based classifiers, and fuse the classification results with either an additive, multiplicative or SVM-based technique. As the main result of this paper, we are able to achieve a significant improvement of image categorisation accuracy by applying these generic state-of-the-art image content analysis techniques.