Creating a novel semantic video search engine through enrichment textual and temporal features of subtitled YouTube media fragments

Abstract

Semantic video Annotation is an active research zone within the field of multimedia content understanding. With the stable increase of videos published on the famous video sharing platforms such as YouTube, more efforts are spent to automatically annotate these videos. In this paper, we propose a novel framework to annotating subtitled YouTube videos using both textual features such as all of portions extracted from web natural language processors in relation to subtitles, and temporal features such as the duration of the media fragments where particular entities are spotted. We implement SY-VSE (Subtitled YouTube Video Search Engine) as an efficient framework to cruising on the subtitled YouTube videos resident in the Linked Open Data (LOD) cloud. For realizing this purpose, we propose Unifier Module of Natural Language Processing (NLP) Tools Results (UM-NLPTR) for extracting main portions of the 10 NLP web tools from subtitles associated to YouTube videos in order to generate media fragments annotated with resources from the LOD cloud. Then, we propose Unifier Module of Popular API's Results (UM-PAR) containing the seven favorite web APIs to boost results of Named Entities (NE) obtained from UM-NLPTR. We will use dotNetRDF as a powerful and flexible API for working with Resource Description Framework (RDF) and SPARQL in .Net environments.