Date of Original Version

7-2003

Type

Conference Proceeding

Abstract or Description

In this paper, we explore supervised classification methods for video shot segmentation. We transform the temporal segmentation problem into a multi-class categorization issue. This approach provides a uniform framework for using different kinds of features extracted from the video and for detecting various types of shot boundaries. The approach utilizes manual labeled training data and a simple classification structure, which eliminates arbitrary thresholds and achieves more reliable estimation than previous threshold-based methods. Contrastive experiments on 13 videos (∼4 hours) show excellent performance on the 2001 TREC video track shot classification task in terms of precision and recall.

DOI

10.1109/ICME.2003.1221710

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