  The current article shows how concepts from the areas of random walks, Markov
chains, complex networks and image analysis can be naturally combined in order
to provide a unified and biologically plausible model relating saliency and
visual attention. Two types of models are proposed: (i) images are converted
into complex networks by considering pixels as nodes while connections are
established in terms of fields of influence defined by visual features such as
tangent fields induced by gray-level contrasts and distance; and (ii) image
pixels exhibiting particularly distinctive values of visual properties such as
gray-level intensity, contrast, size of objects, orientation and texture are
mapped into nodes and the weights of links are defined in order to favor
transitions between regions with similar or different visual features, also
taking the distance between the nodes into account. Preferential random walks
are performed on such networks in order to emulate attentional shifts and eye
movements, and the saliency of each region is obtained in terms of the
frequency of visits to each node at equilibrium. In the case of the first
model, there is a definite tendency to emphasize not only high curvature points
but also convergences of the tangent field. The frequency of visits is found to
be strongly correlated with the node degrees (strengths) for this model.
Different results have been obtained for the second model as a consequence of
the directed and asymmetric nature of the respectively obtained networks.
