自监督学习提取2D轮廓特征，对变换保持不变性。
ShapeEmbed: a self-supervised learning framework for 2D contour quantification
- 用欧氏距离矩阵编码轮廓，构建自监督学习框架
- 在自然与生物图像上分类性能优于现有方法
- 适合生物成像等需要几何不变性的场景
物体的形状是众多应用中的重要视觉信息来源。形状量化的核心挑战之一是确保提取的度量在保持对象内在几何不变的变换下保持稳定，如尺寸、方向和位置的变化。本文提出ShapeEmbed，一种自监督表示学习框架，用于将2D图像中的物体轮廓（以欧氏距离矩阵形式表示）编码为对平移、缩放、旋转、反射及点索引变化均不变的形状描述符。该方法克服了传统形状描述符的局限性，并优于现有的基于自编码器的先进方法。我们在自然图像和生物图像上的形状分类任务中验证了所学描述符的优越性，表明该方法在生物成像领域具有重要应用潜力。
原文摘要 · Abstract (English)
The shape of objects is an important source of visual information in a wide range of applications. One of the core challenges of shape quantification is to ensure that the extracted measurements remain invariant to transformations that preserve an object's intrinsic geometry, such as changing its size, orientation, and position in the image. In this work, we introduce ShapeEmbed, a self-supervised representation learning framework designed to encode the contour of objects in 2D images, represented as a Euclidean distance matrix, into a shape descriptor that is invariant to translation, scaling, rotation, reflection, and point indexing. Our approach overcomes the limitations of traditional shape descriptors while improving upon existing state-of-the-art autoencoder-based approaches. We demonstrate that the descriptors learned by our framework outperform their competitors in shape classification tasks on natural and biological images. We envision our approach to be of particular relevance to biological imaging applications.
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