Sports video clustering
2005
- 4Usage
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
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Artifact Description
Video browsing has become more and more challenging over the past couple of years due to increased digital video availability. Without efficient indexing and organization, searching for videos over a huge media library poses a real problem. The goal of this research is to use clustering algorithms to group similar sports videos based on color and camera motion features. Sports genre is suitable for clustering since most sports videos have color and camera motion features that serve as important visual cues and can be used to identify them. A statistical method for combining color and camera motion features for clustering will also be developed. Once the sports videos have been clustered according to the similarity of their features, video retrieval can be done directly on a particular cluster, thus improving retrieval performance.
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