Histograph as a demonstrator for domain specific challenges to crowd-sourcing

Abstract

HistoGraph provides an integrated pipeline for the extraction of co-occurrence information in historical photos to build an exploreable social graph of relationships that can lead to new insights for historical research. The application leverages on the CUbRIK platform for human/machine computation and applies a hybrid approach to face-detection and -recognition that combines the strengths of algorithmic analysis with expert and generic crowd sourcing. Following a general overview of our approach, we explore the surplus value of human touch for the identification of identities in historical image collections through a uniform crowd-sourcing approach. We find that only a combination of generic and expert crowds yields promising results. Even though the application was designed and developed for a specific target audience, we aim not only at demonstrating the current functionality but also identify and discuss several core principles that can be transferred to other domains.