High-dimensional images, or images with a high-dimensional attribute vector per pixel, are commonly explored with coordinated views of a low-dimensional embedding of the attribute space and a conventional image representation. Nowadays, such images can easily contain several million pixels. For such large datasets, hierarchical embedding techniques are better suited to represent the high-dimensional attribute space than flat dimensionality reduction methods. However, available hierarchical dimensionality reduction methods construct the hierarchy purely based on the attribute information and ignore the spatial layout of pixels in the images. This impedes the exploration of regions of interest in the image space, since there is no congruence between a region of interest in image space and the associated attribute abstractions in the hierarchy. In this paper, we present a superpixel hierarchy for high-dimensional images that takes the high-dimensional attribute manifold into account during construction. Through this, our method enables consistent exploration of high-dimensional images in both image and attribute space. We show the effectiveness of this new image-guided hierarchy in the context of embedding exploration by comparing it with classical hierarchical embedding-based image exploration in two use cases.

翻译：高维图像（即每个像素具有高维属性向量的图像）通常通过属性空间的低维嵌入视图与常规图像表示视图的协同方式进行探索。当前，此类图像可轻易包含数百万像素。针对此类大规模数据集，层次化嵌入技术比扁平化的降维方法更适合表征高维属性空间。然而，现有层次化降维方法仅基于属性信息构建层次结构，忽略了像素在图像中的空间分布。这阻碍了图像空间中感兴趣区域的探索，因为图像空间的感兴趣区域与层次结构中对应的属性抽象之间缺乏一致性。本文提出一种面向高维图像的超级像素层次结构，其构建过程充分考虑了高维属性流形的几何特性。该方法通过此设计实现了高维图像在图像空间与属性空间中探索的一致性。我们通过两个应用场景中与传统层次化嵌入图像探索方法的对比，验证了这种新型图像引导层次结构在嵌入探索中的有效性。