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Efficient and effective handling of video documents depends on the availability of indexes. Manual indexing is unfeasible for large video collections. Efficient, single modality based, video indexing methods have appeared in literature. Effective indexing, however, requires a multimodal approach in which either the most appropriate modality is selected or the different modalities are used in collaborative fashion. We present a framework for multimodal video indexing, which views a video document from the perspective of its author. The framework serves as a blueprint for a generic and flexible multimodal video indexing system, and generalizes different state-of-the-art video indexing methods. It furthermore forms the basis for categorizing these different methods.