Incorporating Texture Information into Dimensionality Reduction for High-Dimensional Images
| Autor: | Vieth, A., Vilanova, A., Lelieveldt, B.P.F., Eisemann, E., Höllt, T., O'Conner, L. |
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| Přispěvatelé: | Visual Analytics |
| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: |
Machine Learning
FOS: Computer and information sciences Computer Science - Machine Learning Computer Vision and Pattern Recognition (cs.CV) Computer Science - Computer Vision and Pattern Recognition Human-centered computing-Visual analytics Human-centered computing-Visualization techniques Computer Vision and Pattern Recognition Machine Learning (cs.LG) Mathematics of computing-Dimensionality reduction |
| Zdroj: | Proceedings-2022 IEEE 15th Pacific Visualization Symposium, PacificVis 2022 arXiv, 2022:2202.09179. Cornell University Library |
| ISSN: | 2331-8422 |
| Popis: | 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. 10 pages main paper, 8 pages supplemental material. To appear at IEEE 15th Pacific Visualization Symposium 2022 |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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