Attention Feature Network Extraction Combined with the Generation Algorithm of Multimedia Image Description. (28th September 2021)
- Record Type:
- Journal Article
- Title:
- Attention Feature Network Extraction Combined with the Generation Algorithm of Multimedia Image Description. (28th September 2021)
- Main Title:
- Attention Feature Network Extraction Combined with the Generation Algorithm of Multimedia Image Description
- Authors:
- Sun, Beibei
- Other Names:
- Mu Zhendong Academic Editor.
- Abstract:
- Abstract : In view of the issue that the features of the images in the shallow layer cannot be fully utilized when the image description is generated and the target association of the image cannot be sufficiently obtained, a generation method for the description of the acquisition of attention images is put forward in this paper. The proportions of the features of images at various depths are autonomously assigned based on the content data of the language model, and the images thus generated are all pictures with image features with attention. In this way, the effect of description generation of images has been improved. After the testing of the database, the results indicate that the calculation method of the algorithm put forward in this paper is more accurate than the top-down multimedia image algorithm generated by a single attention.
- Is Part Of:
- Advances in multimedia. Volume 2021(2021)
- Journal:
- Advances in multimedia
- Issue:
- Volume 2021(2021)
- Issue Display:
- Volume 2021, Issue 2021 (2021)
- Year:
- 2021
- Volume:
- 2021
- Issue:
- 2021
- Issue Sort Value:
- 2021-2021-2021-0000
- Page Start:
- Page End:
- Publication Date:
- 2021-09-28
- Subjects:
- Multimedia systems -- Periodicals
Computer networks -- Periodicals
Multimédia
Réseaux d'ordinateurs
Computer networks
Multimedia systems
Periodicals
006.7 - Journal URLs:
- https://www.hindawi.com/journals/am/ ↗
http://bibpurl.oclc.org/web/22854 ↗ - DOI:
- 10.1155/2021/6484128 ↗
- Languages:
- English
- ISSNs:
- 1687-5680
- Deposit Type:
- Legaldeposit
- View Content:
- Available online (eLD content is only available in our Reading Rooms) ↗
- Physical Locations:
- British Library HMNTS - ELD Digital store
- Ingest File:
- 19495.xml