LAMP Seminar
Language and Media Processing Laboratory
Conference Room 4406
A.V. Williams Building
University of Maryland

Characterization of video content

Sami Nieminen
April 14, 1998

Efficient procedures for browsing, filtering, sorting or retrieving pictorial content require accurate content characterization. Methods for automatically classifying a video streams into categories are presented.

In the first method motion information is extracted from a video sequence and used to train Hidden Markov Models to classify sports and news video sequences and different types of movie trailers.

The second method uses local activity estimation and shot length values to classify movie trailers according to their activity/violence characteristics with a simple 2-D classifier.

Results and directions for future research are discussed.

N. Vasconcelos and A. Lippman. Towards Semantically Meaningful Feature Spaces for the Characterization of Video Content, ICIP'97, Santa Barbara, California, 1997, IEEE.

G. Iyengar and A. Lippman. Models for automatic classification of video sequences. SPIE vol. 3312, pp. 216-227, Photonics West '98, San Jose, California 1998.

The other papers you were interested in are:

Joshua S. Wachman (1996) TR#383: A Video Browser That Learns by Example Appears in: Masters Thesis, MIT Media Lab

N. Vasconcelos and A. Lippman. A Bayesian Video Modeling Framework for Shot Segmentation and Content Characterization, Wkshp on CAIVL, CVPR'97, San Juan, Puerto Rico, 1997, © IEEE.

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