  In the interactive image segmentation task, the Particle Competition and
Cooperation (PCC) model is fed with a complex network, which is built from the
input image. In the network construction phase, a weight vector is needed to
define the importance of each element in the feature set, which consists of
color and location information of the corresponding pixels, thus demanding a
specialist's intervention. The present paper proposes the elimination of the
weight vector through modifications in the network construction phase. The
proposed model and the reference model, without the use of a weight vector,
were compared using 151 images extracted from the Grabcut dataset, the PASCAL
VOC dataset and the Alpha matting dataset. Each model was applied 30 times to
each image to obtain an error average. These simulations resulted in an error
rate of only 0.49\% when classifying pixels with the proposed model while the
reference model had an error rate of 3.14\%. The proposed method also presented
less error variation in the diversity of the evaluated images, when compared to
the reference model.
