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Abstract:
In this study, we used three computational algorithms to compute basis sets for natural
image patches, such that each patch could be synthesized as a linear combination of basis functions.
The two biologically plausible algorithms non-negative matrix factorization (NMF) and sparsenet
(SPN) were compared to standard principal component analysis (PCA). We assessed human
psychophysical performance at identifying natural image patches synthesized using different basis set
sizes in each of the algorithms. We also computed the reconstruction error, which represents a simple
objective measure of synthesis performance. We found that the reconstruction error was a good
predictor of human psychophysical performance. Performance was best for PCA, followed by NMF
and SPN despite large differences in basis function characteristics. All algorithms were well able to
generalize to represent novel natural image patches. When applied to white noise patches instead of
natural images, PCA and SPN outperformed NMF. This shows that of the three algorithms the one
that is least biologically plausible (PCA) actually supported best psychophysical performance,
suggesting that in the present study it is low-level quality of reconstruction that is the main
determinant of psychophysical performance.
