  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.
