A variational model for image classification and restoration
Samson, C.
Blanc-Feraud, L.
Aubert, G.
Zerubia, J.
INRIA, Sophia Antipolis;
This paper appears in: Pattern Analysis and Machine Intelligence, IEEE Transactions on
Publication Date: May 2000
Volume: 22,
Issue: 5
On page(s): 460-472
ISSN: 0162-8828
References Cited: 51
CODEN: ITPIDJ
INSPEC Accession Number: 6674552
Digital Object Identifier: 10.1109/34.857003
Current Version Published: 2002-08-06
Abstract
We present a variational model devoted to image classification
coupled with an edge-preserving regularization process. The discrete
nature of classification (i.e., to attribute a label to each pixel) has
led to the development of many probabilistic image classification
models, but rarely to variational ones. In the last decade, the
variational approach has proven its efficiency in the field of
edge-preserving restoration. We add a classification capability which
contributes to provide images composed of homogeneous regions with
regularized boundaries, a region being defined as a set of pixels
belonging to the same class. The soundness of our model is based on the
works developed on the phase transition theory in mechanics. The
proposed algorithm is fast, easy to implement, and efficient. We compare
our results on both synthetic and satellite images with the ones
obtained by a stochastic model using a Potts regularization
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