An Interactive Hybrid Image Segmentation Based on PCC and Region Approach

Semi-supervised learning is applied to classification problems where only a small portion of the data items is labeled. In these cases, the reliability of the labels is a crucial factor, because mislabeled items may propagate wrong labels to a large portion or even the entire data set. In the interactive image segmentation problem, a human specialist labels some pixels of an object while the semi-supervised algorithm labels the remaining pixels of the segment. The particle competition and cooperation model is a recent graph-based semi-supervised learning approach. It employs particles walking in a graph to classify the data items corresponding to graph nodes. Each particle group aims to dominate most unlabeled nodes, spreading their label, and preventing enemy particles invasion. Region-based image segmentation is an important preprocessing step for high-level computer vision tasks. This is used to present an approach to image partition into regions that reflect the objects in a scene. In this paper an interactive hybrid image segmentation technique to be based on particle competition- cooperation and region based similarity is proposed. This technique has combined effect of particle and region based approach. The results are checked with parameters like error rate.