Conference Proceedings

An evaluation of sparseness as a criterion for selecting independent component filters, when applied to texture retrieval

N Mohammed, DMG Squire

2014 International Conference on Digital Image Computing: Techniques and Applications, DICTA 2014 | Published : 2015


In this paper we evaluate the utility of sparseness as a criterion for selecting a sub-set of independent component filters (ICF). Four sparseness measures were presented more than a decade ago by Le Borgne et al., but have since been ignored for ICF selection. In this paper we present our evaluation in the context of texture retrieval. We compare the sparseness-based method with the dispersal-based method, also proposed by Le Borgne et al., and the clustering-based method previously proposed by us. We show that the sparse filters and highly dispersed filters are quite different. In fact we show that highly dispersed filters tend to have lower sparseness. We also show that the sparse filters..

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University of Melbourne Researchers