Evaluation framework for Image Segmentation Algorithms

• URL: http://arxiv.org/abs/2504.04435v1
• Date: Sun, 06 Apr 2025 10:20:26 GMT
• Title: Evaluation framework for Image Segmentation Algorithms
• Authors: Tatiana Merkulova, Bharani Jayakumar,
• Abstract summary: This paper introduces the fundamental concepts and importance of image segmentation, and the role of interactive segmentation in enhancing accuracy.<n>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.<n>A comparative analysis presents detailed results, emphasizing the strengths, limitations, and trade-offs of each method.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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

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