Mathematics – Statistics Theory
Scientific paper
2012-03-09
Annals of Statistics 2011, Vol. 39, No. 6, 2912-2935
Mathematics
Statistics Theory
Published in at http://dx.doi.org/10.1214/11-AOS925 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of
Scientific paper
10.1214/11-AOS925
Image segmentation is a long-studied and important problem in image processing. Different solutions have been proposed, many of which follow the information theoretic paradigm. While these information theoretic segmentation methods often produce excellent empirical results, their theoretical properties are still largely unknown. The main goal of this paper is to conduct a rigorous theoretical study into the statistical consistency properties of such methods. To be more specific, this paper investigates if these methods can accurately recover the true number of segments together with their true boundaries in the image as the number of pixels tends to infinity. Our theoretical results show that both the Bayesian information criterion (BIC) and the minimum description length (MDL) principle can be applied to derive statistically consistent segmentation methods, while the same is not true for the Akaike information criterion (AIC). Numerical experiments were conducted to illustrate and support our theoretical findings.
Aue Alexander
Lee Thomas C. M.
No associations
LandOfFree
On image segmentation using information theoretic criteria does not yet have a rating. At this time, there are no reviews or comments for this scientific paper.
If you have personal experience with On image segmentation using information theoretic criteria, we encourage you to share that experience with our LandOfFree.com community. Your opinion is very important and On image segmentation using information theoretic criteria will most certainly appreciate the feedback.
Profile ID: LFWR-SCP-O-302439