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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Chengde Zhang Xiao Wu Mei-Ling Shyu Qiang Peng |
| Copyright Year | 2013 |
| Abstract | News web videos exhibit several characteristics, including a limited number of features, noisy text information, and error in near-duplicate keyframes (NDK) detection. Such characteristics have made the mining of the events from news web videos a challenging task. In this paper, a novel framework is proposed to better group the associated web videos to events. First, the data preprocessing stage performs feature selection and tag relevance learning. Next, multiple correspondence analysis is applied to explore the correlations between terms and events with the assistance of visual information. Cooccurrence and visual near-duplicate feature trajectory induced from NDKs are combined to calculate the similarity between NDKs and events. Finally, a probabilistic model is proposed for news web video event mining, where both visual temporal information and textual distribution information are integrated. Experiments on the news web videos from YouTube demonstrate that the integration of visual temporal information and textual distribution information outperforms the existing methods in the news web video event mining. |
| Starting Page | 124 |
| Ending Page | 135 |
| Page Count | 12 |
| File Size | 2173540 |
| File Format | |
| ISSN | 21682291 |
| Volume Number | 46 |
| Issue Number | 1 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2016-01-01 |
| Publisher Place | U.S.A. |
| Access Restriction | One Nation One Subscription (ONOS) |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Visualization Trajectory Feature extraction Data mining Correlation Probabilistic logic Noise measurement visual feature trajectory Cooccurrence multiple correspondence analysis (MCA) near-duplicate keyframes (NDK) news web video event mining |
| Content Type | Text |
| Resource Type | Article |
| Subject | Artificial Intelligence Signal Processing Human Factors and Ergonomics Control and Systems Engineering Computer Networks and Communications Human-Computer Interaction Computer Science Applications |
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