Improved random walker algorithm for image segmentation

General purpose image segmentation is one of the important and challenging problems in image processing. Objective of image segmentation is to group regions with coherent cues such as intensity, texture, color and shape together. Most of the earlier studies on this issue are based on supervised and unsupervised learning methods. In this paper, we develop a semi-supervised image segmentation technique for images using filter bank responses as features. This study utilizes a graph based semi-supervised random walker algorithm to perform segmentation task. Filter bank response driven random walker algorithm has not been considered in the past. We present segmentation results using a variety of images to demonstrate the effectiveness of the proposed technique.