Multiplicity project at the 123data exhibition in Paris

Multiplicity is a collective photographic portrait of Paris. Idealized and designed by Moritz Stefaner, in the occasion of the 123 data exhibition, this interactive installation provides an immersive dive into the image space spanned by hundreds of thousands of photos taken across the Paris city area and shared on social media.

Content selection and curation aspects

The original image dataset consisted of 6.2m geo-located social media photos posted in Paris in 2017. However, for a not really clarified reason (maybe a technical aspect?), a custom selection of 25.000 photos was chosen according to a list of criteria. Moritz highlights it was his intention not to measure, but portray the city. He says: “Rather than statistics, the project presents a stimulating arrangement of qualitative contents, open for exploration and to interpretation — consciously curated and pre-arranged, but not pre-interpreted.” This curated method wasn’t just used for data selection but also for bridging the t-SNE visualization and the grid visualization. Watch the transition effect in the video below. As a researcher interested in user interface and visualization techniques to support knowledge discovery in digital image collections, I wonder if a curated-applied method could be considered in a Digital Humanities approach.

Data Processing

Using machine learning techniques, the images are organized by similarity and image contents, allowing to visually explore niches and microgenres of image styles and contents. More precisely, it uses t-SNE dimensionality reduction to visualize the features from the last layer of a pre-trained neural network to cluster images of Paris. The author says: “I used feature vectors normally intended for classification to calculate pairwise similarities between the images. The map arrangement was calculated using t-SNE — an algorithm that finds an optimal 2D layout so that similar images are close together.”

While the t-SNE algorithm takes care of the clustering and neighborhood structure, manual annotations help with identification of curated map areas. These areas can be zoomed on demand enabling close viewing of similar photos.

 

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