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On sparsity and overcompleteness in image models

Berkes, P; Turner, R; Sahani, M; (2009) On sparsity and overcompleteness in image models. In:

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Computational models of visual cortex, and in particular those based on sparse coding, have enjoyed much recent attention. Despite this currency, the question of how sparse or how over-complete a sparse representation should be, has gone without principled answer. Here, we use Bayesian model-selection methods to address these questions for a sparse-coding model based on a Student-t prior. Having validated our methods on toy data, we find that natural images are indeed best modelled by extremely sparse distributions; although for the Student-t prior, the associated optimal basis size is only modestly over-complete.

Type: Proceedings paper
Title: On sparsity and overcompleteness in image models
ISBN: 160560352X
URI: http://discovery.ucl.ac.uk/id/eprint/175484
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