AI 中文总结
该研究对13种从经典到深度的镜像对称评分方法进行基准测试，涵盖多个数据集和协议。结果显示深度主干表现最佳，但经典HOG描述符也不差且速度更快。判别集中在中等尺度特征，现有方法中冻结深度特征优势不明显，任务训练的深度评分器效果待察，还开源了相关工具。
AI 中文摘要
量化图像关于给定轴的镜像对称程度（对称评分）对从视觉美学到医学成像等应用至关重要，但此前的评分方法从未在通用且基于统计的协议上进行比较。我们对13种评分方法进行基准测试，涵盖从经典特征到冻结深度特征的方法，在四个单轴和五个多轴数据集上，采用反射精确协议，通过机会锚定、显著性检验的判别技能。深度主干在单轴和更难的多轴协议上表现最佳，但经典的方向梯度直方图（HOG）描述符以小差距（但显著）落后于最佳冻结网络读出，与第二名（一种CNN滤波器测量）在统计上无显著差异，且在CPU上运行速度快约300倍。结果表明判别集中在中等尺度的方向特征上，现有方法中，冻结深度特征在测量对称方面相比调优后的经典描述符优势不大，任务训练的深度评分器能否表现更好仍未可知。我们在imgsym中发布了评分器和工具，这是一个用于图像对称检测和测量的开源工具包。
英文摘要
Quantifying how mirror-symmetric an image is about a given axis (symmetry scoring) underpins applications from visual aesthetics to medical imaging, yet proposed scoring methods have never been compared on a common, statistically grounded protocol. We benchmark 13 scoring methods (9 collected from the literature; 4 introduced here) spanning from classical features to frozen deep features, across four single-axis and five multi-axis datasets under a reflection-exact protocol with a chance-anchored, significance-tested discrimination skill. Deep backbones perform best on single-axis and harder multi-axis protocols. However, a classical histogram-of-oriented-gradients (HOG) descriptor trails the best frozen-network readout by a small (but significant) margin, is not statistically separable from the runner-up (a CNN-filter measure), and runs $\sim$300$\times$ faster on CPU. Our results show that discrimination concentrates in mid-scale oriented features, where deep backbones peak at a low or mid stage, and HOG peaks at a mid cell size. Among existing methods, frozen deep features thus offer little over a tuned classical descriptor for measuring symmetry; whether task-trained deep scorers can do better remains open. We release the scorers and harness, in imgsym, an open toolkit for image symmetry detection and measurement.
Commentsv2: matches the published journal version; 24 pages, 6 figures, 7 tables. Code and benchmark: https://github.com/maxwoe/imgsym
Journal refSymmetry 2026, 18(8), 1355
