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

TL;DR

This paper introduces a visual-interactive system that learns and models users' subjective similarity perceptions of complex objects, exemplified by soccer players, enabling personalized and effective similarity search.

Contribution

It presents a novel visual-interactive approach that actively learns user-specific similarity models for complex data objects like soccer players.

Findings

Effective learning of user-defined similarity models

Successful validation through usage scenarios and cross-validation

Enhanced retrieval accuracy for personalized similarity search

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.