Multimodal search for effective video retrieval

• Authors: Apostol (Paul) Natsev
• Venue: CIVR'06 Proceedings of the 5th international conference on Image and Video Retrieval
• Year: 2006

Semantic search and retrieval of multimedia content is a challenging research field that has drawn significant attention in the multimedia research community. With the dramatic growth of digital media at home, in enterprises, and on the web, methods for effective indexing and search of visual content are vital in unlocking the value of this content. Conventional database search and text search over large textual corpora are both well-understood problems with ubiquitous applications. However, search in non-textual unstructured content, such as image and video data, is not nearly as mature or effective. A common approach for video retrieval, for example, is to apply conventional text search techniques to the associated closed caption or speech transcript. This approach works fairly well for retrieving named entities, such as specific people, objects, or places. However, it does not work well for generic topics related to general settings, events, or people actions, as the speech track rarely describes the background setting or the visual appearance of the subject. Text-based search is not even applicable to scenarios that do not have speech transcripts or other textual metadata for indexing purposes (e.g., consumer photo collections). In addition, speech-based video retrieval frequently leads to false matches of segments that talk about but do not depict the entity of interest. Because of these and other limitations, it is now apparent that conventional text search techniques on their own are not sufficient for effective image and video retrieval, and they need to be combined with techniques that consider the visual semantics of the content. The most substantial work in this field is presented in the TREC Video Retrieval Evaluation (TRECVID) community, which focuses its efforts on evaluating video retrieval approaches by providing common video datasets and a standard set of queries.