  Particle competition and cooperation (PCC) is a graph-based semi-supervised
learning approach. When PCC is applied to interactive image segmentation tasks,
pixels are converted into network nodes, and each node is connected to its
k-nearest neighbors, according to the distance between a set of features
extracted from the image. Building a proper network to feed PCC is crucial to
achieve good segmentation results. However, some features may be more important
than others to identify the segments, depending on the characteristics of the
image to be segmented. In this paper, an index to evaluate candidate networks
is proposed. Thus, building the network becomes a problem of optimizing some
feature weights based on the proposed index. Computer simulations are performed
on some real-world images from the Microsoft GrabCut database, and the
segmentation results related in this paper show the effectiveness of the
proposed method.
