Introducing Context and Reasoning in Visual Content Analysis: An Ontology-Based Framework

The amount of multimedia content produced and made available on the World Wide Web, and in professional and, not least, personal collections, is constantly growing, resulting in equally increasing needs in terms of efficient and effective ways to access it. Enabling smooth access at a level that meets user expectations and needs has been the holy grail in content-based retrieval for decades as it is intertwined with the so-called semantic gap between the features that can be extracted from such content through automatic analysis and the conveyed meaning as perceived by the end users. Numerous efforts towards more reliable and effective visual content analysis that target the extraction of user-oriented content descriptions have been reported, addressing a variety of domains and applications, and following diverse methodologies. Among the reported literature, knowledge-based approaches utilising explicit, a priori, knowledge constitute a popular choice aiming at analysis methods decoupled from application-specific implementations. Such knowledge may address various aspects including visual characteristics and numerical representations, topological knowledge about the examined domain, contextual knowledge, as well as knowledge driving the selection and execution of the processing steps required. Among the different knowledge representations adopted in the reported literature, ontologies, being the key enabling technology of the Semantic Web (SW) vision for knowledge sharing and reuse through machine processable metadata, have been favoured in recent efforts. Indicative state-of-the-art approaches include, among others, the work presented in Little and Hunter (2004), and Hollink, Little and Hunter (2005), where ontologies have been used to represent objects of the