An efficient probabilistic context-free parsing algorithm that computes prefix probabilities
Computational Linguistics
Discovery and Segmentation of Activities in Video
IEEE Transactions on Pattern Analysis and Machine Intelligence
Recognition of Visual Activities and Interactions by Stochastic Parsing
IEEE Transactions on Pattern Analysis and Machine Intelligence
Image Indexing Using Color Correlograms
CVPR '97 Proceedings of the 1997 Conference on Computer Vision and Pattern Recognition (CVPR '97)
Deixis and conjunction in multimodal systems
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
SeLeCT: a lexical cohesion based news story segmentation system
AI Communications - STAIRS 2002
Fine-grained hidden markov modeling for broadcast-news story segmentation
HLT '01 Proceedings of the first international conference on Human language technology research
Content-based video indexing of TV broadcast news using hidden Markov models
ICASSP '99 Proceedings of the Acoustics, Speech, and Signal Processing, 1999. on 1999 IEEE International Conference - Volume 06
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The objective of the work reported here is to provide an automatic, context-of-capture categorization, structure detection and segmentation of news broadcasts employing a multimodal semantic based approach. We assume that news broadcasts can be described with context-free grammars that specify their structural characteristics. We propose a system consisting of two main types of interoperating units: The recognizer unit consisting of several modules and a parser unit. The recognizer modules (audio, video and semantic recognizer) analyze the telecast and each one identifies hypothesized instances of features in the audiovisual input. A probabilistic parser analyzes the identifications provided by the recognizers. The grammar represents the possible structures a news telecast may have, so the parser can identify the exact structure of the analyzed telecast.