发现经典阈值方法在评估时存在偏倚，影响结果可信度。
Image Thresholding: Understanding Bias of Evaluation Metrics towards Specific Evaluation Functions
- 通过分析所有阈值下指标与目标函数的相关性，揭示评估偏差。
- Otsu法在PSNR和SSIM上相关性均高于Kapur法，尤其在91%图像上更优。
- 提醒研究者需警惕评估指标对方法的隐含偏好，适合算法评估者参考。
多级图像阈值分割广泛应用于医学成像到遥感等领域。经典目标函数如Otsu类间方差和Kapur熵常通过元启发式算法优化，并以结构相似性指数（SSIM）和峰值信噪比（PSNR）评估性能。本文检验了这些指标是否无偏的假设，分析了BSDS500数据集所有可能阈值下目标函数与评价指标的相关性。结果显示，Otsu准则与SSIM和PSNR始终具有高相关性，而Kapur熵的相关性较弱且波动较大。在全部图像中，Otsu在PSNR上的相关性优于Kapur；在超过91%的图像中，其在SSIM上的相关性也更高。研究揭示了评价指标与目标函数间的固有偏倚，强调需要更中立的评估框架，并建议扩展至更多阈值准则与应用领域。代码可于https://w3id.org/met-dp/icpr26-95获取。
原文摘要 · Abstract (English)
Multilevel image thresholding is widely used for segmentation in applications ranging from medical imaging to remote sensing. Classical objective functions, such as Otsu's between-class variance and Kapur's entropy, are often optimized using metaheuristic algorithms, with performance evaluated via metrics like Structural Similarity Index (SSIM) and Peak Signal-to-Noise Ratio (PSNR). These evaluations implicitly assume that SSIM and PSNR provide unbiased measures of segmentation quality. In this study, we examine this assumption by analyzing the correlation between thresholding objective functions and quality metrics across all possible thresholds for images in the BSDS500 dataset. Results show that Otsu's criterion consistently exhibits high correlation with both SSIM and PSNR, while Kapur's entropy demonstrates weaker and more variable correlation. Otsu outperforms Kapur in correlation with PSNR for all images and with SSIM for over 91%. Our findings reveal an inherent metric-objective-function bias. This work highlights the need for more neutral evaluation frameworks and motivates extending the analysis to additional thresholding criteria and domains. Source code of this paper can be found at https://w3id.org/met-dp/icpr26-95
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。