We propose a novel probabilistic framework for semantic video in(cid:173) dexing. We define probabilistic multimedia objects (multijects) to map low-level media features to high-level semantic labels. The main contribution is a novel application of a factor graph framework to model this network. Using the sum-product algorithm [1] for approximate or exact inference in these factor graph multinets, we attempt to correct errors made during isolated concept detec(cid:173) tion by forcing high-level constraints.

We propose a novel probabilistic framework for semantic video indexing. Wedefine probabilistic multimedia objects (multijects) to map low-level media features to high-level semantic labels.

We propose a novel probabilistic framework for semantic video indexing. We define probabilistic multimedia objects (multijects) to map low-level media features to high-level semantic labels.

We propose a novel probabilistic framework for semantic video indexing. We define probabilistic multimedia objects (multijects) to map low-level media features to high-level semantic labels.