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Evaluation framework for Image Segmentation Algorithms

Tatiana Merkulova, Bharani Jayakumar

TL;DR

This paper addresses evaluating segmentation methods across naive, ML, and DL families with a focus on interactive segmentation. It introduces a framework that measures initial and refined performance using $IoU$ alongside computation and user interaction times, enabling fair comparisons between automated, interactive, and hybrid approaches. It shows that DL methods such as U-Net and Mask R-CNN achieve the highest accuracy at the cost of computational resources, while naive methods remain fast baselines and ML methods offer a middle ground. The findings inform practical method selection for different application needs and point to future directions, including real-time feedback and weakly/self-supervised learning to improve efficiency and robustness.

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

Evaluation framework for Image Segmentation Algorithms

TL;DR

This paper addresses evaluating segmentation methods across naive, ML, and DL families with a focus on interactive segmentation. It introduces a framework that measures initial and refined performance using alongside computation and user interaction times, enabling fair comparisons between automated, interactive, and hybrid approaches. It shows that DL methods such as U-Net and Mask R-CNN achieve the highest accuracy at the cost of computational resources, while naive methods remain fast baselines and ML methods offer a middle ground. The findings inform practical method selection for different application needs and point to future directions, including real-time feedback and weakly/self-supervised learning to improve efficiency and robustness.

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

Paper Structure

This paper contains 20 sections, 1 equation, 5 figures, 1 table.

Table of Contents

  1. Introduction
  2. Background Theory
  3. Implementation and Experiments
  4. Evaluation Metrics
  5. Intersection over Union (IoU)
  6. Computation Time
  7. User Interaction Time
  8. Comprehensive Analysis
  9. Additional Considerations
  10. Results
  11. Performance Analysis
  12. Quantitative Results
  13. Detailed Observations
  14. Deep Learning (DL) Methods:
  15. Machine Learning (ML) Approaches:
  16. ...and 5 more sections

Figures (5)

  • Figure 1: Illustration of a hybrid approach combining user and algorithm interaction
  • Figure 2: Illustration of algorithm assisting user in image segmentation
  • Figure 3: Illustration of user assisting algorithm in image segmentation
  • Figure 4: Illustration of a hybrid approach combining user and algorithm interaction
  • Figure 5: Comparative Performance Analysis: (a) Initial IoU Scores for Naive CV, ML, and DL methods, (b) Max Refined IoU Scores, (c) Alpha vs Beta.