The process of image segmentation is one of the most important steps in computer vision for image retrieval, visual summary, image-based modeling and in many other processes. The goal of segmentation is typically to locate certain objects of interest. In this paper, we have studied and investigated graph based normalized cut segmentation methods and proposed a technique for adding flexibility to the parameters for performance improvement. These methods are examined analytically and tested their performance for the standard images. The results obtained for the important metrics show that these methods perform better than others approach and are computationally efficient, and useful for precise image segmentation.
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