Applying fuzzy clustering method to color image segmentation

The goal of this paper was to apply fuzzy clustering algorithm known as Fuzzy C-Means to color image segmentation, which is an important problem in pattern recognition and computer vision. For computational experiments, serial and parallel versions were implemented. Both were tested using various parameters and random number generator seeds. Various distance measures were used: Euclidean, Manhattan metrics and two versions of Gower coefficient similarity measure. The F and Q segmentation evaluation measures and output images were used to assess the result of color segmentation. Serial and parallel run times were compared.