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Proceedings Paper

Data reduction procedure for principal cast and other talking head detection
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Paper Abstract

We describe a technique for reducing the data set for a technique for reducing the data set for principal cast and other taking head detection in broadcast news content using the spatial attributes of MPEG-7 Motion Activity descriptor. The fact that these descriptors are easy to extract from compressed domain and also work well when used for matching talking head sequences, motivated us to utilize them for rapidly pruning the data set for subsequent sophisticated face detection techniques. We are thus able to speed up the process of finding the principal cast from broadcast news content by reducing the number of segments on which computationally more expensive face detection and recognition is employed. We present the experimental results of two from the centroid of ground truth set and is computationally less expensive. The second clustering procedure is based on multiple templates, which are the mean feature vectors of the component Gaussians of a Gaussian Mixture Model (GMM) trained best to fit the training data. We are able to save 50% on computation measured in terms of number of rejected shots to total number of shots while missing 25% of talking head shots in the news program. We also observe that the second clustering procedure while being slightly computationally intensive allows for higher pruning factors with more accuracy.

Paper Details

Date Published: 19 December 2001
PDF: 6 pages
Proc. SPIE 4676, Storage and Retrieval for Media Databases 2002, (19 December 2001); doi: 10.1117/12.451089
Show Author Affiliations
Ajay Divakaran, Mitsubishi Electric Research Labs. (United States)
Regunathan Radhakrishnan, Mitsubishi Electric Research Labs. (United States)


Published in SPIE Proceedings Vol. 4676:
Storage and Retrieval for Media Databases 2002
Minerva M. Yeung; Chung-Sheng Li; Rainer W. Lienhart, Editor(s)

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