An Evaluation of Labelling-Game Data for Video Retrieval

Abstract

Games with a purpose (GWAPs) are increasingly used in audio-visual collections as a mechanism for annotating videos through tagging. This trend is driven by the assumption that user tags will improve video search. In this paper we study whether this is indeed the case. To this end, we create an evaluation dataset that consists of: (i) a set of videos tagged by users via video labelling game, (ii) a set of queries derived from real-life query logs, and (iii) relevance judgements. Besides user tags from the labelling game, we exploit the existing metadata associated with the videos (textual descriptions and curated in-house tags) and closed captions. Our findings show that search based on user tags alone outperforms search based on all other metadata types. Combining user tags with the other types of metadata yields an increase in search performance of 33%. We also find that the search performance of user tags steadily increases as more tags are collected.