Characterisation and adaptive learning in interactive video retrieval
Rubén Fernández Beltrán · 2016
Retrieving videos by content is a very challenging task because it involves a wide variety of fields.From low-level video descriptors to high-level visual understanding, Content-based Video Retrieval (CBVR) systems have to fill a huge semantic gap to provide users with those videos which satisfy their queries.Even though some of the state-of-the-art approaches have shown to be successful on reduced databases, the ongoing expansion of video collections demands new capabilities in CBVR.Retrieval systems are required to be more efficient to deal with this increasing amount of samples and more effective to cope with more complex query concepts.In this thesis, we explore how difficult this task is and how our contributions try to improve the current state-of-the-art.