Analysis of automatic news segmentation combining with conditional random field knowledge recognition algorithm
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| Veröffentlicht in: | Signal, Image and Video Processing vol. 18, no. 4 (Jun 2024), p. 3867 |
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Springer Nature B.V.
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| 001 | 3256978403 | ||
| 003 | UK-CbPIL | ||
| 022 | |a 1863-1703 | ||
| 022 | |a 1863-1711 | ||
| 024 | 7 | |a 10.1007/s11760-024-03048-w |2 doi | |
| 035 | |a 3256978403 | ||
| 045 | 2 | |b d20240601 |b d20240630 | |
| 100 | 1 | |a Xiao, Chenghong |u Jilin Province Economic Management Cadre College, Changchun City, China | |
| 245 | 1 | |a Analysis of automatic news segmentation combining with conditional random field knowledge recognition algorithm | |
| 260 | |b Springer Nature B.V. |c Jun 2024 | ||
| 513 | |a Journal Article | ||
| 520 | 3 | |a With the continuous development of the social economy, the ways people obtain news information are becoming increasingly diversified, but with that comes too much data. The research on Extracting useful information from too much data is extremely effective. Given these needs and deficiencies, this paper introduces a conditional random field knowledge recognition algorithm, designing a Segmentation analysis model for audience news with the segmentation technology of key frames and shots by sorting the business logic of automatic news Segmentation, realizing the analysis of the news video picture., and then analyze the news Segmentation to ensure that the production and dissemination of news programs are intelligent and smart. The simulation experiment results show that the conditional random field knowledge recognition algorithm is effective and can effectively support the analysis of automatic news Segmentation. | |
| 653 | |a Algorithms | ||
| 653 | |a Segmentation | ||
| 653 | |a Conditional random fields | ||
| 653 | |a News media | ||
| 653 | |a Recognition | ||
| 653 | |a Television news | ||
| 653 | |a Probability distribution | ||
| 653 | |a Knowledge | ||
| 773 | 0 | |t Signal, Image and Video Processing |g vol. 18, no. 4 (Jun 2024), p. 3867 | |
| 786 | 0 | |d ProQuest |t Advanced Technologies & Aerospace Database | |
| 856 | 4 | 1 | |3 Citation/Abstract |u https://www.proquest.com/docview/3256978403/abstract/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text |u https://www.proquest.com/docview/3256978403/fulltext/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |
| 856 | 4 | 0 | |3 Full Text - PDF |u https://www.proquest.com/docview/3256978403/fulltextPDF/embedded/7BTGNMKEMPT1V9Z2?source=fedsrch |