Post-Hoc Interactive Analytics of Errors in the Context of a Person Discovery Task

Part of the research effort in automatic person discovery in multimedia content consists in analyzing the errors made by algorithms. However exploring the space of models relating algorithmic errors in person discovery to intrinsic properties of associated shots (e.g. person facing the camera) - coined as post-hoc analysis in this paper - requires data curation and statistical model tuning, which can be cumbersome. In this paper we present a visual and interactive tool that facilitates this exploration. A case study is conducted with multimedia researchers to validate the tool. Real data obtained from the MediaEval person discovery task was used for this experiment. Our approach yielded novel insight that was completely unsuspected previously.