Selecting the Right Metric: A Detailed Study on Image Segmentation Evaluation

Abstract

Image segmentation is critical role in various computer vision tasks, such as object recognition, medical imaging, autonomous driving, and document analysis. To evaluate the performance of segmentation algorithms, various metrics have been developed, each providing insights into different aspects of segmentation accuracy, object completeness, and boundary precision. This paper presents a comprehensive review of the key evaluation metrics used in image segmentation, including popular metrics such as Intersection over Union, Dice Coefficient, F1-score, and boundary-based metrics like average and maximum boundary distance. We explore the taxonomy of evaluation techniques, distinguishing between supervised and unsupervised methods, and discuss the strengths, limitations, and applications of each metric. Furthermore, we address the challenges associated with image segmentation, including handling noise, occlusions, illumination variation, and the scarcity of annotated datasets for training. By offering a detailed analysis of evaluation metrics, this paper aims to guide researchers and practitioners in selecting appropriate metrics for specific tasks and improving algorithm performance through informed comparisons and parameter tuning.