Modeling natural microimage statistics

Koloydenko, Alexey


Koloydenko, Alexey (2000) Modeling natural microimage statistics.

Our Full Text Deposits

Full text access: Open

Full text file - 1.26 MB

Links to Copies of this Item Held Elsewhere


A large collection of digital images of natural scenes provides a database for analyzing and modeling small scene patches (e.g., 2 x 2) referred to as natural microimages. A pivotal ¯nding is the stability of the empirical microimage distribution across scene samples and with respect to scaling. With a view toward potential applications (e.g. classi¯cation, clutter modeling, segmentation), we present a hierarchy of microimage probability models which capture essential local image statistics. Tools from information theory, algebraic geometry and of course statistical hypothesis testing are employed to assess the "match" between candidate models and the empirical distribution. Geometric symmetries play a key role in the model selection process. One central result is that the microimage distribution exhibits reflection and rotation symmetry and is well-represented by a Gibbs law with only pairwise interactions. However, the acceptance of the up-down reflection symmetry hypothesis is borderline and intensity inversion symmetry is rejected. Finally, possible extensions to larger patches via entropy maximization and to patch classification via vector quantization are briefly discussed.

Information about this Version

This is a Submitted version
This version's date is: 2000
This item is not peer reviewed

Link to this Version

Item TypeThesis (Doctoral)
TitleModeling natural microimage statistics
AuthorsKoloydenko, Alexey
DepartmentsFaculty of Science\Mathematics


Deposited by Research Information System (atira) on 18-Nov-2014 in Royal Holloway Research Online.Last modified on 07-Feb-2017