Context Correlation Using Probabilistic Semantics

We present an approach for recognizing highlevel geo-temporal phenomena – referred as events/occurrences– from in-depth discovery of information, using geo-tagged photos, formal event models, and various context cues like weather, space, time, and people. Due to the relative availability of information, our approach automatically obtains a probabilistic measure of occurrence likelihood for the recognized geo-temporal phenomena. This measure, however, is not only used to find the best event among the merely possible candidates – witnessing the data (including photos), but it can also provide informative cues to human operators in the environments where uncertainty is involved in the existing knowledge.