Text-based Semantic Annotation Service for Multimedia Content in the Esperonto project

Within the Esperonto project, an integration of NLP, ontologies and other knowledge bases, is being performed with the goal to implement a semantic annotation service that upgrades the actual Web towards the emerging Semantic Web. Research is being currently conducted on how to apply the Esperonto semantic annotation service to text material associated with still images in web pages. In doing so, the project will allow for semantic querying of still images in the web, but also (automatically) create a large set of text-based semantic annotations of still images, which can be used by the Multimedia community in order to support the task of content indexing of image material, possibly combining the Esperonto type of annotations with the annotations resulting from image analysis

Multimedia Annotations on the Semantic Web

Multimedia in all forms (images, video, graphics, music, speech) is exploding on the Web. The content needs to be annotated and indexed to enable effective search and retrieval. However, recent standards and best practices for multimedia metadata don't provide semantically rich descriptions of multimedia content. On the other hand, the World Wide Web Consortium's (W3C's) Semantic Web effort has been making great progress in advancing techniques for annotating semantics of Web resources. To bridge this gap, a new W3C task force has been created to investigate multimedia annotations on the Semantic Web. This article examines the problems of semantically annotating multimedia and describes the integration of multimedia metadata with the Semantic Web. (Editor's note by John R. Smith)

Requirements for practical multimedia annotation

Applications that use annotated multimedia assets need to be able to process all the annotations about a specific media asset. At first sight, this seems almost trivial, but annotations are needed for different levels of description, these need to be related to each other in the appropriate way and, in particular on the Semantic Web, annotations may not all be stored in the same place. We distinguish between technical descriptions of a media asset from content-level descriptions. At both levels, the annotations needed in a single application may come from different vocabularies. In addition, the instantiated values for a term used from an ontology also need to be specified. We present a number of existing vocabularies related to multimedia

Bridging the Semantic Gap in Multimedia Information Retrieval: Top-down and Bottom-up approaches

Semantic representation of multimedia information is vital for enabling the kind of multimedia search capabilities that professional searchers require. Manual annotation is often not possible because of the shear scale of the multimedia information that needs indexing. This paper explores the ways in which we are using both top-down, ontologically driven approaches and bottom-up, automatic-annotation approaches to provide retrieval facilities to users. We also discuss many of the current techniques that we are investigating to combine these top-down and bottom-up approaches