title: Visual embedding: a model for visualization.
abstract: The authors propose visual embedding as a model for automatically generating and evaluating visualizations. A visual embedding is a function from data points to a space of visual primitives that measurably preserves structures in the data (domain) within the mapped perceptual space (range). The authors demonstrate its use with three examples: coloring of neural tracts, scatterplots with icons, and evaluation of alternative diffusion tensor glyphs. They discuss several techniques for generating visual-embedding functions, including probabilistic graphical models for embedding in discrete visual spaces. They also describe two complementary approaches--crowdsourcing and visual product spaces--for building visual spaces with associated perceptual--distance measures. In addition, they recommend several research directions for further developing the visual-embedding model.
authors.forename: Çağatay
authors.initials: Ç
authors.lastname: Demiralp
authors.forename: Carlos E
authors.initials: CE
authors.lastname: Scheidegger
authors.forename: Gordon L
authors.initials: GL
authors.lastname: Kindlmann
authors.forename: David H
authors.initials: DH
authors.lastname: Laidlaw
authors.forename: Jeffrey
authors.initials: J
authors.lastname: Heer