Incorporating Texture Information into Dimensionality Reduction for High-Dimensional Images

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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.

Original languageEnglish
Title of host publicationProceedings - 2022 IEEE 15th Pacific Visualization Symposium, PacificVis 2022
EditorsL. O'Conner
Place of PublicationPiscataway
Number of pages10
ISBN (Electronic)978-1-6654-2335-9
ISBN (Print)978-1-6654-2336-6
Publication statusPublished - 2022
Event2022 IEEE 15th Pacific Visualization Symposium (PacificVis) - Tsukuba, Japan
Duration: 11 Apr 202214 Apr 2022

Publication series

NameIEEE Pacific Visualization Symposium
ISSN (Print)2165-8765
ISSN (Electronic)2165-8773


Conference2022 IEEE 15th Pacific Visualization Symposium (PacificVis)
Abbreviated titlePacificVis 2022


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


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