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Uploader Intent for Online Video: Typology, Inference and Applications

NameUploader Intent for Online Video: Typology, Inference and Applications

Users upload video for specific reasons, but rarely state these reasons explicitly in the video metadata. Information about the reasons motivating uploaders has the potential ultimately to benefit a wide range of application areas, including video production, video-based advertising , and video search. In this paper, we apply a combination of social-Web mining and crowdsourcing to arrive at a typology that characterizes the uploader intent of a broad range of videos. We then use a set of multimodal features, including visual semantic features, found to be indicative of uploader intent in order to classify videos automatically into uploader intent classes. We evaluate our approach on a dataset containing ca. 3K crowdsourcing-annotated videos and demonstrate its usefulness in prediction tasks relevant to common application areas

is ieee
ieee paper year2016
price rangemedium
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