Visual-Interactive Similarity Search for Complex Objects by Example of Soccer Player Analysis
This addresses the problem of subjective similarity in data analysis for domain-specific users, but it is incremental as it builds on existing interactive and active learning methods.
The paper tackles the challenge of defining subjective similarity for complex objects like soccer players by introducing a visual-interactive system that learns users' mental models through labeling and active learning, enabling downstream retrieval tasks with demonstrated applicability in evaluations.
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.