TY - GEN
T1 - Incorporating Texture Information into Dimensionality Reduction for High-Dimensional Images
AU - Vieth, A.
AU - Vilanova, A.
AU - Lelieveldt, B.
AU - Eisemann, E.
AU - Höllt, T.
PY - 2022
Y1 - 2022
N2 - 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.
AB - 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.
KW - Mathematics of computing-Dimensionality reduction
KW - Human-centered computing-Visualization techniques
KW - Human-centered computing-Visual analytics
UR - http://www.scopus.com/inward/record.url?scp=85132424377&partnerID=8YFLogxK
U2 - 10.1109/PacificVis53943.2022.00010
DO - 10.1109/PacificVis53943.2022.00010
M3 - Conference contribution
SN - 978-1-6654-2336-6
T3 - IEEE Pacific Visualization Symposium
SP - 11
EP - 20
BT - Proceedings - 2022 IEEE 15th Pacific Visualization Symposium, PacificVis 2022
A2 - O'Conner, L.
PB - IEEE
CY - Piscataway
T2 - 2022 IEEE 15th Pacific Visualization Symposium (PacificVis)
Y2 - 11 April 2022 through 14 April 2022
ER -