Optimizing Weight Functions for Enhanced Image Segmentation Using Normalized Cut

Effective image segmentation remains a fundamental challenge in computer vision, with the Normalized Cut (Ncut) method emerging as a powerful technique for partitioning images into meaningful segments. The efficacy of Ncut largely depends on the choice of the weight function, which quantifies the similarity between image elements, be the pixels or predefined regions. This paper presents a novel framework to optimize the weight functions for Ncut in the context of image segmentation, aiming to bridge the gap between theoretical robustness and practical applicability. We first discussed the theoretical aspects of Ncut, emphasizing the role of weight functions in achieving segmentation that is both globally and locally suitable. Subsequently, we analyze the frameworks for the systematic selection of weight functions, effective to different image characteristics such as texture, color, and spatial relationships. Our methodology combining color spaces analysis, texture descriptors, and edge information. Through several experimentations on Corel and Berkley image segmentation datasets, including natural scenes and images, we demonstrate the comparisons of the weight functions over conventional methods in terms of segmentation quality and evaluated with standard algorithms like Otsu thresholding and C-means clustering algorithm. Three validity indices have been used to quantify the results and observe the superiority of the proposed model. This work not only advances the understanding of weight function optimization in Ncut-based image segmentation but also offers a practical guide for researchers and practitioners in computer vision.