Collection Space Navigator

The Collection Space Navigator (CSN) is an explorative visualization tool for researching collections and their multidimensional representations. We designed this tool to better understand multidimensional data, its methods, and semantic qualities through spatial navigation and filtering. CSN can be used with any image collection and can be customized for specific research needs (see Jupyter Notebook or Google Colab).

Links

🖥️ Online demo
💾 Code
📄 Research paper
🌐 Project website

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, for e.g. based on metadata or using machine learning to embed image information. Yet, while such procedures are widespread for a range of applications, it remains a challenge to explore, analyze, and understand the resulting multidimensional spaces in a more comprehensive manner. Dimensionality reduction techniques such as t-SNE or UMAP often serve to project high-dimensional data into low dimensional visualizations, yet require interpretation themselves as the remaining dimensions are typically abstract. Here, the Collection Space Navigator provides a customizable interface that combines two-dimensional projections with a set of configurable multidimensional filters. As a result, the user is able to view and investigate collections, by zooming and scaling, by transforming between projections, by filtering dimensions via range sliders, and advanced text filters. Insights that are gained during the interaction can be fed back into the original data via ad hoc exports of filtered metadata and projections. This paper comes with a functional showcase demo using a large digitized collection of classical Western art. The Collection Space Navigator is open source. Users can reconfigure the interface to fit their own data and research needs, including projections and filter controls. The CSN is ready to serve a broad community.

Figures

Acknowledgements

Tillmann Ohm and Mar Canet Solà designed, co-authored, and developed the Collection Space Navigator (CSN) software. Tilmann Ohm, Mar Canet Solà, Anders Karjus, Maximilian Schich contributed to the broader research design, including initial applications of the CSN. The authors further thank the members of the CUDAN Research Group for useful discussions. All authors are supported by ERA Chair for Cultural Data Analytics, funded through the European Union’s Horizon 2020 research and innovation program (Grant No.810961). The CSN code is partly based on the umap-explorer by GrantCuster.