Fellner, Dieter ; Kohlhammer, Jörn ; Zeppelzauer, Matthias ; Sessler, David ; Ritter, Christian ; Bernard, Jürgen (2017)
Visual-Interactive Similarity Search for Complex Objects by Example of Soccer Player Analysis.
12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications. Porto, Portugal (27.02.2017-01.03.2017)
doi: 10.5220/0006116400750087
Konferenzveröffentlichung, Bibliographie
Kurzbeschreibung (Abstract)
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.
| Typ des Eintrags: | Konferenzveröffentlichung |
|---|---|
| Erschienen: | 2017 |
| Autor(en): | Fellner, Dieter ; Kohlhammer, Jörn ; Zeppelzauer, Matthias ; Sessler, David ; Ritter, Christian ; Bernard, Jürgen |
| Art des Eintrags: | Bibliographie |
| Titel: | Visual-Interactive Similarity Search for Complex Objects by Example of Soccer Player Analysis |
| Sprache: | Englisch |
| Publikationsjahr: | 2017 |
| Veranstaltungstitel: | 12th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications |
| Veranstaltungsort: | Porto, Portugal |
| Veranstaltungsdatum: | 27.02.2017-01.03.2017 |
| DOI: | 10.5220/0006116400750087 |
| URL / URN: | https://doi.org/10.5220/0006116400750087 |
| Kurzbeschreibung (Abstract): | 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. |
| Freie Schlagworte: | Information visualization, Visual analytics, Similarity search, Feature selection, Complex data, Information retrieval, Active learning |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Graphisch-Interaktive Systeme |
| Hinterlegungsdatum: | 07 Mai 2020 09:40 |
| Letzte Änderung: | 04 Feb 2022 12:38 |
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