Publication

Bridging the Gap between Semantics and Multimedia Processing

Abstract

In this paper, we overview the semantic gap problem in multimedia and discuss how machine learning and symbolic AI can be combined to narrow this gap. We describe the semantic gap in terms of a classical architecture for multimedia processing and discuss a structured approach to bridge it. Such an approach combines machine learning (for mapping signals to objects) and symbolic AI (for linking objects to meaning). Our main goal is to raise awareness and discuss the challenges involved in such a structured approach to multimedia understanding, especially in the view of the latest developments in machine learning and symbolic AI.

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Classical architecture usually denotes architecture which is more or less consciously derived from the principles of Greek and Roman architecture of classical antiquity, or sometimes more specifically, from the works of the Roman architect Vitruvius. Different styles of classical architecture have arguably existed since the Carolingian Renaissance, and prominently since the Italian Renaissance. Although classical styles of architecture can vary greatly, they can in general all be said to draw on a common "vocabulary" of decorative and constructive elements.
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