Affiliation:
1. University of Amsterdam, Amsterdam, The Netherlands
2. Carnegie Mellon University
Abstract
We propose a generic and robust framework for news video indexing which we founded on a broadcast news production model. We identify within this model four production phases, each providing useful metadata for annotation. In contrast to semiautomatic indexing approaches which exploit this information at production time, we adhere to an automatic data-driven approach. To that end, we analyze a digital news video using a separate set of multimodal detectors for each production phase. By combining the resulting production-derived features into a statistical classifier ensemble, the framework facilitates robust classification of several
rich
semantic concepts in news video; rich meaning that concepts share many similarities in their production process. Experiments on an archive of 120 hours of news video from the 2003 TRECVID benchmark show that a combined analysis of production phases yields the best results. In addition, we demonstrate that the accuracy of the proposed style analysis framework for classification of several rich semantic concepts is state-of-the-art.
Publisher
Association for Computing Machinery (ACM)
Subject
Computer Networks and Communications,Hardware and Architecture
Reference39 articles.
1. Toward automatic extraction of expressive elements from motion pictures: tempo
2. Semantic indexing of multimedia content using visual, audio, and text cues;Adams W. H.;EURASIP J. Appl. Signal Process.,2003
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