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Visual-hint Boundary to Segment Algorithm for Image Segmentation
Image segmentation has been a very active research topic in image analysis
area. Currently, most of the image segmentation algorithms are designed based
on the idea that images are partitioned into a set of regions preserving
homogeneous intra-regions and inhomogeneous inter-regions. However, human
visual intuition does not always follow this pattern. A new image segmentation
method named Visual-Hint Boundary to Segment (VHBS) is introduced, which is
more consistent with human perceptions. VHBS abides by two visual hint rules
based on human perceptions: (i) the global scale boundaries tend to be the real
boundaries of the objects; (ii) two adjacent regions with quite different
colors or textures tend to result in the real boundaries between them. It has
been demonstrated by experiments that, compared with traditional image
segmentation method, VHBS has better performance and also preserves higher
computational efficiency.Comment: 45 page
Unsupervised Image Segmentation using the Deffuant-Weisbuch Model from Social Dynamics
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.Comment: This paper is under consideration at Signal Image and Video
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