Bridging the Gap between Semantics and Multimedia Processing
This work addresses the semantic gap problem for researchers and practitioners in multimedia processing, but it is incremental as it builds on existing methods without presenting new experimental results.
The paper tackles the semantic gap problem in multimedia by proposing a structured approach that combines machine learning for signal-to-object mapping and symbolic AI for object-to-meaning linking, aiming to raise awareness and discuss challenges in multimedia understanding.
In this paper, we give an overview of the semantic gap problem in multimedia and discuss how machine learning and symbolic AI can be combined to narrow this gap. We describe the gap in terms of a classical architecture for multimedia processing and discuss a structured approach to bridge it. This approach combines machine learning (for mapping signals to objects) and symbolic AI (for linking objects to meanings). Our main goal is to raise awareness and discuss the challenges involved in this structured approach to multimedia understanding, especially in the view of the latest developments in machine learning and symbolic AI.