A feature selection framework for video semantic recognition via integrated cross-media analysis and embedded learning
Abstract Video data are usually represented by high dimensional features. The performance of video semantic recognition, however, may be deteriorated due to the irrelevant and redundant components included into the high dimensional representations. To improve the performance of video semantic recogn...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
SpringerOpen
2019-02-01
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| Series: | EURASIP Journal on Image and Video Processing |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s13640-019-0428-5 |