论文标题
最佳传输网络中的不混溶颜色流动图像分类
Immiscible Color Flows in Optimal Transport Networks for Image Classification
论文作者
论文摘要
在分类任务中，有意义地利用数据中包含的信息至关重要。尽管解决这些任务的大部分工作都致力于建立复杂的算法基础架构以以黑盒方式处理输入，但在将其输入到算法之前，如何利用如何利用数据的各个方面。在这里，我们通过提出一个由物理启发的动力系统提出的，该系统适应了最佳的传输原理，以有效利用图像的颜色分布，从而将其关注。我们的动态调节在图像构建的网络上传播的颜色的不混溶通量。它没有将颜色汇总在一起，而是将它们视为与边缘共享容量相互作用的不同商品。然后可以将所得的最佳流动送入标准分类器中，以区分不同类别的图像。我们展示了我们的方法如何在颜色信息重要的数据集中胜过图像分类任务的竞争方法。
In classification tasks, it is crucial to meaningfully exploit the information contained in data. While much of the work in addressing these tasks is devoted to building complex algorithmic infrastructures to process inputs in a black-box fashion, less is known about how to exploit the various facets of the data, before inputting this into an algorithm. Here, we focus on this latter perspective, by proposing a physics-inspired dynamical system that adapts Optimal Transport principles to effectively leverage color distributions of images. Our dynamics regulates immiscible fluxes of colors traveling on a network built from images. Instead of aggregating colors together, it treats them as different commodities that interact with a shared capacity on edges. The resulting optimal flows can then be fed into standard classifiers to distinguish images in different classes. We show how our method can outperform competing approaches on image classification tasks in datasets where color information matters.
