Estimation of priors in natural images
We investigate prior distributions in natural images by using Boltzmann machine, to find some possible universal properties and individual characteristics of natural images. For simplicity, we specifically focus on binary pictures. We find that in most cases there emerges a structure with two sublattices, and the nearest-neighbor and next-nearest-neighbor interactions correspondingly take two discriminative values, which reflects individual characteristics of each set of pictures. On the other hand, in a longer spacial scale, a longer-range (though still rapidly-decaying) ferromagnetic interaction commonly appear in all the cases. The characteristic length scale of this longer-range interaction is universally about up to four lattice spacing ξ ≈ 4. These results are derived by using the mean-field method which effectively reduces the computational time required in Boltzmann machine. An improved mean-field method called the Bethe approximation also gives the same result, which reinforces the validity of our analysis and findings.