Incorporating Texture Information into Dimensionality Reduction for High-Dimensional Images

Vieth, A.; Vilanova, A.; Lelieveldt, B.P.F.; Eisemann, E.; Höllt, T.

doi:10.1109/PacificVis53943.2022.00010

Incorporating Texture Information into Dimensionality Reduction for High-Dimensional Images

Title

Incorporating Texture Information into Dimensionality Reduction for High-Dimensional Images

Author

Vieth, A. (TU Delft Computer Graphics and Visualisation)
Vilanova, A. (Eindhoven University of Technology)
Lelieveldt, B.P.F. (Leiden University Medical Center)
Eisemann, E. (TU Delft Computer Graphics and Visualisation)
Höllt, T. (TU Delft Computer Graphics and Visualisation)

Contributor

O'Conner, L. (editor)

Date

2022

Abstract

High-dimensional imaging is becoming increasingly relevant in many fields from astronomy and cultural heritage to systems biology. Visual exploration of such high-dimensional data is commonly facilitated by dimensionality reduction. However, common dimensionality reduction methods do not include spatial information present in images, such as local texture features, into the construction of low-dimensional embeddings. Consequently, exploration of such data is typically split into a step focusing on the attribute space followed by a step focusing on spatial information, or vice versa. In this paper, we present a method for incorporating spatial neighborhood information into distance-based dimensionality reduction methods, such as t-Distributed Stochastic Neighbor Embedding (t-SNE). We achieve this by modifying the distance measure between high-dimensional attribute vectors associated with each pixel such that it takes the pixel's spatial neighborhood into account. Based on a classification of different methods for comparing image patches, we explore a number of different approaches. We compare these approaches from a theoretical and experimental point of view. Finally, we illustrate the value of the proposed methods by qualitative and quantitative evaluation on synthetic data and two real-world use cases.

Subject

Mathematics of computing-Dimensionality reduction
Human-centered computing-Visualization techniques
Human-centered computing-Visual analytics

To reference this document use:

http://resolver.tudelft.nl/uuid:01088dd0-a4e9-4803-a287-f7bd41e52fe8

DOI

https://doi.org/10.1109/PacificVis53943.2022.00010

Publisher

IEEE, Piscataway

ISBN

978-1-6654-2336-6

Source

Proceedings - 2022 IEEE 15th Pacific Visualization Symposium, PacificVis 2022