Text Mining Support for Semantic Indexing and Analysis of A/V Streams


The work described here concerns the use of complementary resources in sports video analysis; soccer in our case. Structured web data such as match tables with teams, player names, score goals, substitutions, etc. and multiple, unstructured, textual web data sources (minute-by-minute match reports) are processed with an ontology-based information extraction tool to extract and annotate events and entities according to the SmartWeb soccer ontology. Through the temporal alignment of the primary A/V data (soccer videos) with the textual and structured complementary resources, these extracted and semantically organized events can be used as indicators for video segment extraction and semantic classification, i.e. occurrences of particular events in the complementary resources can be used to classify the corresponding video segment, enabling semantic indexing and retrieval of soccer videos.

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@inproceedings{Nemrava2008TextMS, title={Text Mining Support for Semantic Indexing and Analysis of A/V Streams}, author={Jan Nemrava and Paul Buitelaar and Thierry Declerck}, year={2008} }