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  • @inproceedings{MoS2014,
    	vgclass =	{refpap},
    	author =	{Nabeel Mohammed and Squire, David McG.},
    	title =	{An evaluation of sparseness as a criterion for selecting
    	independent component filters, when applied to texture retrieval},
    	booktitle =	{Proceedings of the International Conference on Digital
    	Image Computing: Techniques and Applications (DICTA 2014)},
    	address =	{Wollongong, Australia},
    	month =	{November~25--27},
    	year =	{2014},
    	doi =	{},
    	abstract =	{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 give better results
    	compared to the highly dispersed filters when applied to texture
    	retrieval. However the sparseness measures are calculated over filter
    	response energies, making this method susceptible to choosing a
    	redundant filter set. This issue is demonstrated and we show that ICF
    	selected using our clustering-based method, which chooses a filter set
    	with much lower redundancy, outperforms the sparse filters.},