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Video indexing approaches such as visual concept classification and personrecognition are essential to enable fine-grained semantic search in large-scalevideo archives such as the historical video collection of former GermanDemocratic Republic (GDR) maintained by the German Broadcasting Archive (DRA).Typically, a lexicon of visual concepts has to be defined for semantic search.However, the definition of visual concepts can be more or less subjective dueto individually differing judgments of annotators, which may have an impact onannotation quality and subsequently training of supervised machine learningmethods. In this paper, we analyze the inter-coder agreement for historical TVdata of the former GDR for visual concept classification and personrecognition. The inter-coder agreement is evaluated for a group of expert aswell as non-expert annotators in order to determine differences in annotationhomogeneity. Furthermore, correlations between visual recognition performanceand inter-annotator agreement are measured. In this context, information aboutimage quantity and agreement are used to predict average precision for conceptclassification. Finally, the influence of expert vs. non-expert annotationsacquired in the study are used to evaluate person recognition.

Investigating Correlations of Inter-coder Agreement and Machine Annotation Performance for Historical Video Data