Knowledge-Driven Video Information Retrieval with LOD

In parallel with the tremendously increasing number of video contents on the Web, many technical specifications and standards have been introduced to store technical details and describe the content of, and add subtitles to, online videos. Some of these specifications are based on unstructured data with limited machine-processability, data reuse, and interoperability, while others are XML-based, representing semi-structured data. While low-level video features can be derived automatically, high-level features are mainly related to a particular knowledge domain and heavily rely on human experience, judgment, and background. One of the approaches to solve this problem is to map standard, often semi-structured, vocabularies, such as that of MPEG-7, to machine-interpretable ontologies. Another approach is to introduce new multimedia ontologies. While video contents can be annotated efficiently with terms defined by structured LOD datasets, such as DBpedia, ontology standardization would be desired in the video production and distribution domains. This paper compares the state-of-the-art video annotations in terms of descriptor level and machine-readability, highlights the limitations of the different approaches, and makes suggestions towards standard video annotations.