Known-item search in video databases with textual queries

Abstract

In this paper, we present two approaches for known-item search in video databases with textual queries. In the first approach, we require the database objects to be labeled with an arbitrary ImageNet classification model. During the search, the set of query words is expanded with synonyms and hypernyms until we encounter words present in the database which are consequently searched for. In the second approach, we delegate the query to an independent database such as Google Images and let the user pick a suitable result for query-by-example search. Furthermore, the effectiveness of the proposed approaches is evaluated in a user study.