Probabilistic Semantic Video Indexing

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. A graphical network of such multijects (multinet) captures scene con(cid: 173) text by discovering intra-frame as well as inter-frame dependency relations between the concepts. The main contribution is a novel application of a factor graph framework to model this network. We model relations between semantic concepts in terms of their co-occurrence as well as the temporal dependencies between these concepts within video shots. 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. This results in a significant improvement in the overall detection performance.

Authors

• Milind Naphade
• Igor Kozintsev
• Thomas Huang

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