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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Mi-Mi Lu Lei Xie Zhong-Hua Fu Dong-Mei Jiang Yan-Ning Zhang |
| Copyright Year | 2010 |
| Description | Author affiliation: Shaanxi Provincial Key Laboratory of Speech and Image Information Processing, School of Computer Science, Northwestern Polytechnical University, Xi'an, China (Mi-Mi Lu; Lei Xie; Zhong-Hua Fu; Dong-Mei Jiang; Yan-Ning Zhang) |
| Abstract | This paper investigates how to integrate multi-modal features for story boundary detection in broadcast news. The detection problem is formulated as a classification task, i.e., classifying each candidate into boundary/non-boundary based on a set of features. We use a diverse collection of features from text, audio and video modalities: lexical features capturing the semantic shifts of news topics and audio/video features reflecting the editorial rules of broadcast news. We perform a comprehensive evaluation on boundary detection performance for six popular classifiers, including decision tree (DT), Bayesian network (BN), naive Bayesian (NB) classifier, multi-layer peceptron (MLP), support vector machines (SVM) and maximum entropy (ME) classifier. Results show that BN and DT can generally achieve superior performances over other classifiers and BN offers the best F1-measure. Analysis of BN and DT reveals important inter-feature dependencies and complementarities that contribute significantly to the performance gain. |
| Starting Page | 420 |
| Ending Page | 425 |
| File Size | 607544 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781424462445 |
| e-ISBN | 9781424462469 |
| DOI | 10.1109/ISCSLP.2010.5684854 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2010-11-29 |
| Publisher Place | Taiwan |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Support vector machines Bayesian methods Semantics story segmentation Feature extraction Speech topic detection and tracking Face multi-modal Niobium feature integration story boundary detection |
| Content Type | Text |
| Resource Type | Article |
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