Search results for key=MoS2014 :
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Refereed full papers (journals, book chapters, international conferences)
2014
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Nabeel Mohammed and David McG. Squire,
An evaluation of sparseness as a criterion for selecting
independent component filters, when applied to texture retrieval,
In Proceedings of the International Conference on Digital
Image Computing: Techniques and Applications (DICTA 2014),
Wollongong, Australia, November 25-27 2014.
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.
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