Problem
- In practice, there is a lot of simple rules that can operate
on a combination of extracted image features (obtained via image
analysis) and additional image-related data.
- The implicit meaning in image data can thus be made explicit.
- We study the issue of how to infer (only) meaningful facts from
networked image data.
- In the non-distributed case, the semantics of some centralized
image data can be regarded model-theoretically, as the set of
facts whose deduction is sanctioned by the given rules.
- For example, rules can infer the transitive closure of a centralized
'connected' attribute. In the distributed case, we usually have an
open network of image data, for which no model-theoretic semantics
exists.
- In the example, the transitive closure of a distributed
multi-sensor-derived 'connected' attribute may never form a single
stable set.
