论文标题
可解释的图像聚类通过差异性感知k均值
Interpretable Image Clustering via Diffeomorphism-Aware K-Means
论文作者
论文摘要
我们设计了一种可解释的聚类算法，了解图像歧管的非线性结构。我们的方法利用了在图像空间中应用的$ k $ - 平均值的解释性，同时解决了其集群性能问题。具体而言，我们开发了图像和质心之间相似性的度量，该尺寸包括一般变形类别：差异性，使它们不变。我们的工作利用薄板样条插值技术有效地学习图像歧管的最佳形态。广泛的数值模拟表明，我们的方法与各种数据集上的最新方法竞争。
We design an interpretable clustering algorithm aware of the nonlinear structure of image manifolds. Our approach leverages the interpretability of $K$-means applied in the image space while addressing its clustering performance issues. Specifically, we develop a measure of similarity between images and centroids that encompasses a general class of deformations: diffeomorphisms, rendering the clustering invariant to them. Our work leverages the Thin-Plate Spline interpolation technique to efficiently learn diffeomorphisms best characterizing the image manifolds. Extensive numerical simulations show that our approach competes with state-of-the-art methods on various datasets.
