Author(s): Swati Dewan, Shubham Agarwal, Navjyoti Singh
Abstract: Over the last decade, the volume of user-generated content on the web has skyrocketed. The Semantic Web however, hasn’t grown at the same rate especially when considering videos. This is primarily due to high costs associated with time and resources to manually annotate such data. Here, we leverage the advancements in Machine Learning to reduce these costs by building a faster multimedia annotation system for videos for the Semantic Web. We propose a semi-automatic annotation model which automatically generates semantic annotations over a big dataset of videos using only a small number of manually annotated clips per semantic category. We provide a new semantically annotated dataset on ballet and test our model on it. High-level concepts such as ballet pose and steps are used to make the semantic library. These also act as descriptive meta-tags for any ballet video making the videos retrievable using a semantic or video query.
Keywords: Automatic Annotation; Semantic Annotation; Ballet; Laban Movement Analysis; Meta-data Tagging; Machine Learning; Retrieval; Neural Networks