Semantic Hierarchical Exploration of Large Image Datasets

Abstract

We present a method for exploring and comparing large sets of images with metadata using a hierarchical interaction approach. Browsing many images at the same time requires either a large screen space or an abundance of scrolling interaction. We address this problem by projecting the images onto a two-dimensional Cartesian coordinate system by combining the latent space of vision neural networks and dimensionality reduction techniques. To alleviate overdraw of the images, we integrate a hierarchical layout and navigation, where each group of similar images is represented by the image closest to the group center. Advanced interactive analysis of images in relation to their metadata is enabled through integrated, flexible filtering based on expressions. Furthermore, groups of images can be compared through selection and automated aggregated metadata visualization. We showcase our method in three case studies involving the domains of photography, machine learning, and medical imaging.