First- and second-order information in natural images: a filter-based approach to image statistics.
Previous analyses of natural image statistics have dealt mainly with their Fourier power spectra. Here we explore image statistics by examining responses to biologically motivated filters that are spatially localized and respond to first-order (luminance-defined) and second-order (contrast- or texture-defined) characteristics. We compare the distribution of natural image responses across filter parameters for first- and second-order information. We find that second-order information in natural scenes shows the same self-similarity previously described for first-order information but has substantially less orientational anisotropy. The magnitudes of the two kinds of information, as well as their mutual unsigned correlation, are much stronger for particular combinations of filter parameters in natural images but not in unstructured fractal images having the same power spectra.
Johnson
Aaron P
AP
McGill Vision Research Unit, Department of Ophthalmology, 687 Pine Avenue West, Room H4-14, Montréal, Québec, Canada, H3A 1A1. aaron.johnson1@mcgill.ca
Baker
Curtis L
CL
D000465:Algorithms; D000818:Animals; D032701:Biomimetics / Q000379:methods*; D003198:Computer Simulation; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D016247:Information Storage and Retrieval / Q000379:methods; D008954:Models, Biological; D015233:Models, Statistical; D009415:Nerve Net; D016571:Neural Networks, Computer; D009474:Neurons / Q000502:physiology; D010364:Pattern Recognition, Visual / Q000502:physiology*; D012815:Signal Processing, Computer-Assisted; D014793:Visual Cortex / Q000502:physiology; D014796:Visual Perception / Q000502:physiology