2002
@inproceedings{HZB2002,
vgclass = {refpap},
author = {Lothar Hermes and Thomas Z\"{o}ller and Joachim M.
Buhmann},
title = {Parametric Distributional Clustering for Image
Segmentation},
editor = {Heyden, A. and Sparr, G. and Nielsen, M. and Johansen, P.},
booktitle = {Proceedings of the Seventh European Conference on Computer
Vision (ECCV 2002)},
address = {Copenhagen, Denmark},
volume = {3},
number = {2352},
series = {Lecture Notes in Computer Science},
pages = {577--591},
publisher = {Springer},
month = {May~28--31},
year = {2002},
url = {http://www-dbv.informatik.uni-bonn.de/abstracts/hermes.eccv02.html},
url1 = {http://www-dbv.cs.uni-bonn.de/pdf/hermes.eccv02.pdf},
abstract = {Unsupervised Image Segmentation is one of the central
issues in Computer Vision. From the viewpoint of exploratory data
analysis, segmentation can be formulated as a clustering problem in
which pixels or small image patches are grouped together based on local
feature information. In this contribution, parametrical distributional
clustering (PDC) is presented as a novel approach to image
segmentation. In contrast to noise sensitive point measurements, local
distributions of image features provide a statistically robust
description of the local image properties. The segmentation technique
is formulated as a generative model in the maximum likelihood
framework. Moreover, there exists an insightful connection to the
novel information theoretic concept of the Information Bottleneck
(Tishby et al., 1999), which emphasizes the compromise between
efficient coding of an image and preservation of characteristic
information in the measured feature distributions.
The search for good grouping solutions is posed as an optimization
problem, which is solved by deterministic annealing techniques. In
order to further increase the computational efficiency of the resulting
segmentation algorithm, a multi-scale optimization scheme is developed.
Finally, the performance of the novel model is demonstrated by
segmentation of color images from the Corel data base.},
}