Looking for breakthrough ideas for innovation challenges? Try Patsnap Eureka!

Training method of adaptive conditional random field algorithm for automatically news splitting

A conditional random field and training method technology, applied in computing, computer components, instruments, etc., can solve problems such as poor accuracy, achieve the effect of reducing learning and improving accuracy

Active Publication Date: 2020-06-05
CHENGDU SOBEY DIGITAL TECH CO LTD
View PDF9 Cites 6 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, learning with a fixed step size may cause too much feature data of other non-adjacent news stories to be learned, and cover up the feature data of the current and adjacent news stories. Therefore, the accuracy of the conditional random field algorithm with a fixed step size poor

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Training method of adaptive conditional random field algorithm for automatically news splitting
  • Training method of adaptive conditional random field algorithm for automatically news splitting
  • Training method of adaptive conditional random field algorithm for automatically news splitting

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0054] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, and are not intended to limit the present invention, that is, the described embodiments are only some of the embodiments of the present invention, but not all of the embodiments. The components of the embodiments of the invention generally described and illustrated in the figures herein may be arranged and designed in a variety of different configurations. Accordingly, the following detailed description of the embodiments of the invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely represents selected embodiments of the invention. Based on the embodiments of the present ...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

PUM

No PUM Login to View More

Abstract

The invention discloses a training method of a self-adaptive conditional random field algorithm for automatically news splitting. The method comprises the following steps of: 1, digitizing a news program video, and extracting news splitting feature data and a news splitting label according to the news program video; 2, defining a fixed learning template, and recording learning step lengths including a feature step length and a label step length in the learning template; 3, adaptively adjusting the step length by adopting a heuristic method according to the fixed learning template and the training data news splitting label; and step 4, learning parameters in the conditional random field algorithm by adopting a gradient descent method according to the data learned by the adaptive method in the step 3. According to the method, the current news story feature data and a plurality of previous news story feature data and next news story characteristic data are learned in a self-adaptive manner according to the conditions of the training data, so that learning of the current news story characteristic data and the adjacent news story characteristic data is more focused, learning of non-adjacent news story characteristic data is reduced, and the method has great significance in improving the news automatic splitting accuracy of conditional random field algorithms.

Description

technical field [0001] The invention relates to the field of automatic dismantling of radio and television news, in particular to a training method for an adaptive conditional random field algorithm for automatic dismantling of news. Background technique [0002] In recent years, with the rapid development of my country's radio and television industry, TV news programs have gradually increased. As an important form of information reporting, TV news has the characteristics of wide audience, fast timeliness and high credibility. The entire TV news usually contains multiple news stories, and viewers or video editors usually need to quickly locate one or several news stories, and purely manual search for news stories in TV news videos will take time and effort. Therefore, it is of great significance to find a method that can quickly and automatically split the entire TV news. [0003] As a supervised algorithm, the Conditional Random Field (CRF) algorithm is usually used for l...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to View More

Application Information

Patent Timeline
no application Login to View More
IPC IPC(8): G06K9/00G06K9/62
CPCG06V20/49G06V20/40G06V20/46G06F18/22G06F18/214
Inventor 张诚杨瀚温序铭王炜
Owner CHENGDU SOBEY DIGITAL TECH CO LTD
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Patsnap Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Patsnap Eureka Blog
Learn More
PatSnap group products

Browse by: Latest US Patents, China's latest patents, Technical Efficacy Thesaurus, Application Domain, Technology Topic, Popular Technical Reports.

© 2024 PatSnap. All rights reserved.Legal|Privacy policy|Modern Slavery Act Transparency Statement|Sitemap|About US| Contact US: help@patsnap.com