A shot classification method of selecting effective key-frames for video browsing
MULTIMEDIA '96 Proceedings of the fourth ACM international conference on Multimedia
MULTIMEDIA '00 Proceedings of the eighth ACM international conference on Multimedia
A Fast and Accurate Face Detector Based on Neural Networks
IEEE Transactions on Pattern Analysis and Machine Intelligence
Probabilistic Modeling of Local Appearance and Spatial Relationships for Object Recognition
CVPR '98 Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Comparative Evaluation of Face Sequence Matching for Content-Based Video Access
FG '00 Proceedings of the Fourth IEEE International Conference on Automatic Face and Gesture Recognition 2000
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
Content based indexing of images and video using face detection and recognition methods
ICASSP '01 Proceedings of the Acoustics, Speech, and Signal Processing, 2001. on IEEE International Conference - Volume 03
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IEEE Transactions on Circuits and Systems for Video Technology
Challenges of Image and Video Retrieval
CIVR '02 Proceedings of the International Conference on Image and Video Retrieval
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Information Systems Frontiers
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In an image, the faces of the persons are the first information looked for. Performing efficient face detection in a video with persons (excluding cartoons and nature videos) allows to classify shots, and to obtain automatically face summaries. Shot sampling greatly improves time processing. Scene layout (same number of person, similar face position and size) provides a criterion to establish a similarity measure between shots. Similar shots are gathered within shot clusters and all but one shot of a cluster are discarded from the summaries.