  Unsupervised image segmentation algorithms aim at identifying disjoint
homogeneous regions in an image, and have been subject to considerable
attention in the machine vision community. In this paper, a popular theoretical
model with it's origins in statistical physics and social dynamics, known as
the Deffuant-Weisbuch model, is applied to the image segmentation problem. The
Deffuant-Weisbuch model has been found to be useful in modelling the evolution
of a closed system of interacting agents characterised by their opinions or
beliefs, leading to the formation of clusters of agents who share a similar
opinion or belief at steady state. In the context of image segmentation, this
paper considers a pixel as an agent and it's colour property as it's opinion,
with opinion updates as per the Deffuant-Weisbuch model. Apart from applying
the basic model to image segmentation, this paper incorporates adjacency and
neighbourhood information in the model, which factors in the local similarity
and smoothness properties of images. Convergence is reached when the number of
unique pixel opinions, i.e., the number of colour centres, matches the
pre-specified number of clusters. Experiments are performed on a set of images
from the Berkeley Image Segmentation Dataset and the results are analysed both
qualitatively and quantitatively, which indicate that this simple and intuitive
method is promising for image segmentation. To the best of the knowledge of the
author, this is the first work where a theoretical model from statistical
physics and social dynamics has been successfully applied to image processing.
