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Classification of Non-edited Broadcast Video Using Holistic Low-level Features
Roach Matthew,
Xu Li-qun,
Mason John,
Heath Martlesham,
Ip Ipswich,
Matt Roach 
Int. Tyrrhenian Workshop on Digital Communications (IWDC)
Swansea University Author:
Matt Roach
Abstract
A data driven learning based approach has been adopted to classify a video programme into a set of five pre-defined genre including a7orts, cartoons, news, commercials and music. The video database studied is a collection of five-hour non-edited TV broadcast programme of dozens of video clips: one h...
| Published in: | Int. Tyrrhenian Workshop on Digital Communications (IWDC) |
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| Published: |
2002
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| URI: | https://cronfa.swan.ac.uk/Record/cronfa39129 |
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| Abstract: |
A data driven learning based approach has been adopted to classify a video programme into a set of five pre-defined genre including a7orts, cartoons, news, commercials and music. The video database studied is a collection of five-hour non-edited TV broadcast programme of dozens of video clips: one hour long in total for each genre with an even split of training and testing data. This paper reports the classification result for the two media modes - acoustic and visual - used separately, and for a linear fusion of the two modes at the score level. The correaIonding classification accuracy achieved is approximately 74%, 73%, and 87%, reaIectively, given a 30-second decision window, or for every 30 second non-overlap video segment an answer of its identity is provided Further analysis of the confusion matrices of the classification errors is given. The extension of the approach to the open-set video genre verification and semantic scene segmentation and classification is expected. |
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| College: |
Faculty of Science and Engineering |