Subspace clustering of images using ant colony optimisation

Authors:
Tomas Piatrik;Ebroul Izquierdo
Affiliations:
Queen Mary, University of London, Department of Electronic Engineering, London, UK;Queen Mary, University of London, Department of Electronic Engineering, London, UK
Venue:
ICIP'09 Proceedings of the 16th IEEE international conference on Image processing
Year:
2009

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Abstract

Content-based image retrieval can be dramatically improved by providing a good initial clustering of visual data. The problem of image clustering is that most current algorithms are not able to identify individual clusters that exist in different feature subspaces. In this paper, we propose a novel approach for subspace clustering based on Ant Colony Optimisation and its learning mechanism. The proposed algorithm breaks the assumption that all of the clusters in a dataset are found in the same set of dimensions by assigning weights to features according to the local correlations of data along each dimension. Experiment results on real image datasets show the need for feature selection in clustering and the benefits of selecting features locally.