🤖 AI Summary
This paper addresses the lack of a unified, comprehensive evaluation framework for image segmentation algorithms. We propose the first holistic, interactive assessment framework encompassing traditional methods (e.g., thresholding, edge detection, region growing), machine learning approaches (e.g., random forests, SVM), and deep learning models (e.g., CNNs, U-Net, Mask R-CNN). Innovatively, we systematically integrate three interaction paradigms—“algorithm-assisted user,” “user-assisted algorithm,” and “hybrid interaction”—and establish a multi-objective evaluation metric balancing segmentation accuracy (IoU), computational efficiency (inference time), and human-factor cost (interaction time). We conduct cross-algorithm benchmarking across diverse scenarios using 12 representative methods, revealing inherent trade-offs among accuracy, speed, and interaction overhead. Experimental results demonstrate that the hybrid interaction paradigm achieves up to a 12.3% IoU improvement in complex scenes, thereby filling a critical gap in the quantitative, comparative evaluation of interactive segmentation systems.
📝 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