  The definition of similarity is a key prerequisite when analyzing complex
data types in data mining, information retrieval, or machine learning. However,
the meaningful definition is often hampered by the complexity of data objects
and particularly by different notions of subjective similarity latent in
targeted user groups. Taking the example of soccer players, we present a
visual-interactive system that learns users' mental models of similarity. In a
visual-interactive interface, users are able to label pairs of soccer players
with respect to their subjective notion of similarity. Our proposed similarity
model automatically learns the respective concept of similarity using an active
learning strategy. A visual-interactive retrieval technique is provided to
validate the model and to execute downstream retrieval tasks for soccer player
analysis. The applicability of the approach is demonstrated in different
evaluation strategies, including usage scenarions and cross-validation tests.
