  With the recent advances in complex networks theory, graph-based techniques
for image segmentation has attracted great attention recently. In order to
segment the image into meaningful connected components, this paper proposes an
image segmentation general framework using complex networks based community
detection algorithms. If we consider regions as communities, using community
detection algorithms directly can lead to an over-segmented image. To address
this problem, we start by splitting the image into small regions using an
initial segmentation. The obtained regions are used for building the complex
network. To produce meaningful connected components and detect homogeneous
communities, some combinations of color and texture based features are employed
in order to quantify the regions similarities. To sum up, the network of
regions is constructed adaptively to avoid many small regions in the image, and
then, community detection algorithms are applied on the resulting adaptive
similarity matrix to obtain the final segmented image. Experiments are
conducted on Berkeley Segmentation Dataset and four of the most influential
community detection algorithms are tested. Experimental results have shown that
the proposed general framework increases the segmentation performances compared
to some existing methods.
