Abstract
This paper presents a comprehensive evaluation framework for image segmentation algorithms, encompassing naive methods, machine learning approaches, and deep learning techniques. We begin by introducing the fundamental concepts and importance of image segmentation, and the role of interactive segmentation in enhancing accuracy. A detailed background theory section explores various segmentation methods, including thresholding, edge detection, region growing, feature extraction, random forests, support vector machines, convolutional neural networks, U-Net, and Mask R-CNN. The implementation and experimental setup are thoroughly described, highlighting three primary approaches: algorithm assisting user, user assisting algorithm, and hybrid methods. Evaluation metrics such as Intersection over Union (IoU), computation time, and user interaction time are employed to measure performance. A comparative analysis presents detailed results, emphasizing the strengths, limitations, and trade-offs of each method. The paper concludes with insights into the practical applicability of these approaches across various scenarios and outlines future work, focusing on expanding datasets, developing more representative approaches, integrating real-time feedback, and exploring weakly supervised and self-supervised learning paradigms to enhance segmentation accuracy and efficiency. Keywords: Image Segmentation, Interactive Segmentation, Machine Learning, Deep Learning, Computer Vision
Abstract (translated)
本文提出了一种全面评估图像分割算法的框架，涵盖了简单方法、机器学习方法和深度学习技术。文章首先介绍了图像分割的基本概念及其重要性，并探讨了交互式分割在提高准确性方面的作用。详细的背景理论部分探索了各种分割方法，包括阈值处理、边缘检测、区域增长、特征提取、随机森林、支持向量机、卷积神经网络、U-Net和Mask R-CNN。 实施与实验设置被详尽描述，重点介绍了三种主要的方法：算法辅助用户、用户辅助算法以及混合方法。采用交并比（IoU）、计算时间和用户交互时间等评估指标来衡量性能表现。比较分析部分详细展示了各种方法的结果，并强调了每种方法的优势、局限性及权衡因素。 文章最后总结了这些方法在不同场景中的实际应用价值，并展望未来工作，重点关注扩大数据集规模、开发更具代表性的方法、整合实时反馈以及探索弱监督和自监督学习范式以提高分割准确性和效率。关键词包括：图像分割、交互式分割、机器学习、深度学习、计算机视觉。
URL
https://arxiv.org/abs/2504.04435
