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Free keywords:
Visual analytics, Information visualization
Abstract:
We introduce the Collection Space Navigator (CSN), a browser-based visualization tool to explore, research, and curate large collections of visual digital artifacts that are associated with multidimensional data, such as vector embeddings or tables of metadata. Media objects such as images are often encoded as numerical vectors, based on metadata or using machine learning embeddings. Yet it remains a challenge to explore, analyze, and understand the resulting multidimensional spaces. Dimensionality reduction techniques such as t-SNE or UMAP often serve to project high-dimensional data into low dimensional visualizations, but require interpretation themselves given their typically abstract dimensions. The Collection Space Navigator provides a customizable interface that combines two-dimensional projections with an array of configurable multifunctional filters and navigation controls. The user is able to view and investigate collections by zooming and scaling, transforming between projections, and filtering dimensions via range sliders and text filters. Insights gained through these interactions can be used to augment original data via easy to use export capabilities. This paper comes with a functional online demo showcasing a large digitized collection of classical Western art. Users can reconfigure the interface to fit their own data and research needs, including projections and filter controls. This open source tool is intended to be applicable in a broad range of use cases, types of collections and across diverse disciplines.
