Recently, various crowdsourcing initiatives showed that targeted efforts of user communities result in massive amounts of tags. For example, the Netherlands Institute for Sound and Vision collected a large number of tags with the video labeling game Waisda?. To successfully utilize these tags, a better understanding of their characteristics is required. The goal of this paper is twofold: (i) to investigate the vocabulary that users employ when describing videos and compare it to the vocabularies used by professionals; and (ii) to establish which aspects of the video are typically described and what type of tags are used for this. We report on an analysis of the tags collected with Waisda?. With respect to the first goal, we compared the the tags with a typical domain thesaurus used by professionals, as well as with a more general vocabulary. With respect to the second goal, we compare the tags to the video subtitles to determine how many tags are derived from the audio signal. In addition, we perform a qualitative study in which a tag sample is interpreted in terms of an existing annotation classification framework. The results suggest that the tags complement the metadata provided by professional cataloguers, the tags describe both the audio and the visual aspects of the video, and the users primarily describe objects in the video using general descriptions.

In recent years, crowdsourcing has gained attention as an alternative method for collecting video annotations. An example is the internet video labeling game Waisda? launched by the Netherlands Institute for Sound and Vision. The goal of this PhD research is to investigate the value of the user tags collected with this video labeling game. To this end, we address the following four issues. First, we perform a comparative analysis between user-generated tags and professional annotations in terms of what aspects of videos they describe. Second, we measure how well user tags are suited for fragment retrieval and compare it with fragment search based on other sources like transcripts and professional annotations. Third, as previous research suggested that user tags predominately refer to objects and rarely describe scenes, we will study whether user tags can be successfully exploited to generate scene-level descriptions. Finally, we investigate how tag quality can be characterized and potential methods to improve it.

The goal of the paper is assessing the quality of end-user tags from a video labeling game as a first step in the process of integrating them with the annotations made by professionals. Tags lack precise meaning, whereas the terms and concepts the professionals are used to have a clearly defined semantics given by structured vocabularies. Hence, we explore the possibility of mapping user tags to their semantic counterparts from domain and lexical vocabularies. Furthermore, we report on an analysis performed by a senior cataloguer regarding the general usefulness of the user tags in terms of video and fragment retrieval. Finally, we investigate the distribution of tags with respect to audio and visual portion of the video content.