Institute of Computer Science III
University of Bonn
Databases * Information Systems * Software Engineering *
Pattern Recognition * Image Processing * Artificial Intelligence * Robotics
A Deterministic Annealing Framework for Unsupervised Texture Segmentation
We present a novel framework for unsupervised texture segmentation,
which relies on statistical tests as a measure of homogeneity. Texture
segmentation is formulated as a pairwise data clustering
problem with a sparse neighborhood structure. The pairwise
dissimilarities of texture blocks are computed using a multiscale
image representation based on Gabor filters, which are tuned to spatial
frequencies at different scales and orientations. We derive and
discuss a family of objective functions to pose the segmentation
problem in a precise mathematical formulation. An efficient
optimization method, known as deterministic annealing, is applied to
solve the associated optimization problem. The general framework of
deterministic annealing and meanfield approximation is introduced and
the canonical way to derive efficient algorithms within this framework
is described in detail. Moreover the combinatorial optimization
problem is examined from the viewpoint of scale space theory. The novel algorithm has been extensively tested on Brodatz-like microtexture mixtures and
on real-word images. In addition, benchmark studies with alternative
segmentation techniques are reported.
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16.12.05