An Interactive Hybrid Image Segmentation Based on PCC and Region Approach

Amandeep Kaur, Rupika Rana

Abstract: 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.

Keywords: image segmentation, supervised learning, image partition, computer vision, graph

How to Cite?: Amandeep Kaur, Rupika Rana, "An Interactive Hybrid Image Segmentation Based on PCC and Region Approach", Volume 5 Issue 10, October 2016, International Journal of Science and Research (IJSR), Pages: 952-956, https://www.ijsr.net/getabstract.php?paperid=25081602, DOI: https://dx.doi.org/10.21275/25081602

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