RT Journal Article
SR Electronic
T1 Two-Dimensional Hermite Filters Simplify the Description of High-Order Statistics of Natural Images
JF bioRxiv
FD Cold Spring Harbor Laboratory
SP 060947
DO 10.1101/060947
A1 Qin Hu
A1 Jonathan Victor
YR 2016
UL http://biorxiv.org/content/early/2016/06/27/060947.abstract
AB Natural image statistics play a crucial role in shaping biological visual systems, understanding their function and design principles, and designing effective computer-vision algorithms. High-order statistics are critical for conveying local features, but they are challenging to study – largely because their number and variety is large. Here, via the use of two-dimensional Hermite (TDH) functions, we identify a covert symmetry in high-order statistics of natural images that simplifies this task. This emerges from the structure of TDH functions, which are an orthogonal set of functions that are organized into a hierarchy of ranks. Specifically, we find that the shape (skewness and kurtosis) of the distribution of filter coefficients depends only on the projection of the function onto a 1-dimensional subspace specific to each rank. The characterization of natural image statistics provided by TDH filter coefficients reflects both their phase and amplitude structure, and we suggest an intuitive interpretation for the special subspace within each rank.