Visual-Interactive Similarity Search for Complex Objects by Example of Soccer Player Analysis

Jürgen Bernard, Christian Ritter, David Sessler, Matthias Zeppelzauer, Jörn Kohlhammer, Dieter Fellner

Publikation: Beitrag in Buch/Bericht/KonferenzbandBeitrag in einem Konferenzband


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.
TitelIVAPP 2017
PublikationsstatusVeröffentlicht - 9 Mär 2017
Veranstaltung8th International Conference on Information Visualization Theory and Applications - Porto, Portugal
Dauer: 27 Feb 20171 Mär 2017


Konferenz8th International Conference on Information Visualization Theory and Applications
KurztitelIVAPP 2017

Fields of Expertise

  • Information, Communication & Computing

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