1. Crossing the gap between multimedia data and semantics
DBDM assignment 2
Jasper A. Visser ( )
Bart J. Witteman ( )
Eyal Halm ( )

2. Data and semantics Data is of no value without context
Multimedia data often lacks this context, needs semantics for
Accessibility
Usability
Data mining

3. The problems of multimedia semantics
Data is high volume
Data has high complexity
Data is heterogeneous
Therefore: multimedia semantics are hard to assign

4. Crossing the semantic gap
Different approaches are possible:
Extract features from media automatically (AI):
Data-driven crossing
Adapt and expand the data
Expert couples semantics
Include matching semantics in query:
User-driven crossing
Adapt the query

5. Data-driven semantics
Add metadata to multimedia
Use expert knowledge to train automated systems
Requires linkage of multimedia to metadata
Metadata should be descriptive of actual data
Large amounts of data should be analyzed

6. User-driven semantics
Change the way the user queries the data
Example: picture selection using relevance feedback
Requires appropriate feature sets

7. State of the art Semantic Retrieval of Multimedia Data:
gr/papers/mmdb04.pdf

8. State of the art Learning Semantic Categories for 3D Model Retrieval
itation.cfm?id=

9. State of the art
Combining several approaches to support query processing and relaxation:
Supporting efficient multimedia database exploration: ontent/x4bcw95tnx8pfn90/

10. Further considerations
Temporal data: e.g. still versus moving pictures
Level of detail tradeoffs
Feature selection

11. Questions and discussion