Title
Visual-Interactive Similarity Search For Complex Objects By Example Of Soccer Player Analysis
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.
Year
DOI
Venue
2017
10.5220/0006116400750087
PROCEEDINGS OF THE 12TH INTERNATIONAL JOINT CONFERENCE ON COMPUTER VISION, IMAGING AND COMPUTER GRAPHICS THEORY AND APPLICATIONS (VISIGRAPP 2017), VOL 3
Keywords
DocType
Volume
Information Visualization, Visual Analytics, Active Learning, Similarity Search, Similarity Learning, Distance Measures, Feature Selection, Complex Data Objects, Soccer Player Analysis, Information Retrieval
Conference
abs/1703.03385
Citations 
PageRank 
References 
3
0.38
16
Authors
6
Name
Order
Citations
PageRank
Jürgen Bernard133432.32
Christian Altenhofen282.15
David Sessler392.85
Matthias Zeppelzauer418621.35
Jörn Kohlhammer547438.96
Dieter W. Fellner6780112.84