Exploiting external knowledge to improve video retrieval

Most video retrieval systems are multimodal, commonly relying on textual information, low- and high-level semantic features extracted from query visual examples. In this work, we study the impact of exploiting different knowledge sources in order to automatically retrieve query visual examples relevant to a video retrieval task. Our hypothesis is that the exploitation of external knowledge sources can help on the identification of query semantics as well as on improving the understanding of video contents. We propose a set of techniques to automatically obtain additional query visual examples from different external knowledge sources, such as DBPedia, Flickr and Google Images, which have different coverage and structure characteristics. The proposed strategies attempt to exploit the semantics underlying the above knowledge sources to reduce the ambiguity of the query, and to focus the scope of the image searches in the repositories. We assess and compare the quality of the images obta...

• Type: Conference
• Year: 2010
• Where: MIR
• Authors: David Vallet, Iván Cantador, Joemon M. Jose
• Keywords: External Knowledge Source, Knowledge Source, MIR 2010, Multimedia, Query Visual Examples