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
1. Verfasser: Xiao, Chenghong
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Springer Nature B.V.
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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