The use of data mining and artificial intelligence technology in art colors and graph and images of computer vision under 6G internet of things communication

Ziang Ye () and Lei Su ()
Additional contact information
Ziang Ye: Shenyang Urban Construction University
Lei Su: Chongqing Normal University

International Journal of System Assurance Engineering and Management, 2021, vol. 12, issue 4, No 7, 689-695

Abstract: Abstract To further optimize the existing methods in the field of computer vision and improve the intelligence of image data mining technology, the relevant feedback technology is combined with traditional image data mining technology, and an image data mining technology based on the relevant feedback K-Nearest-Neighbor (K-NN) algorithm is designed and further optimized. The focus is the actual feature extraction test for image color and shape. The test results reveal that for retrieval of K = 1, K = 2, K = 3 images, both positive feedback K-NN algorithm and negative feedback K-NN algorithm can effectively improve the accuracy of image data mining. Among them, negative feedback K-NN algorithm has the highest accuracy for image shape feature extraction. When there are K = 3 images, the accuracy of image data mining can reach 78.3%. Then, the image mining research is conducted on multiple databases. In a total of four databases, the accuracy of image retrieval increases with the increase of feedback times. At the same time, using the optimized KNN algorithm can greatly improve the accuracy of image feature extraction, and the highest accuracy can reach 99.3%. The research content can provide a scientific reference for the follow-up study of KNN algorithm.

Keywords: 6G internet of things communication; Artificial intelligence; Computer vision; Fine art color; Image data mining (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13198-021-01063-5 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ijsaem:v:12:y:2021:i:4:d:10.1007_s13198-021-01063-5

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-021-01063-5

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().