Improving the accuracy of global feature fusion based image categorisation
Ville Viitaniemi and Jorma Laaksonen
In: 2nd International Conference on Semantic and Digital Media Technologies (SAMT 2007), 5-7 Dec 2007, Genova, Italy.

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.

[Edit]