RETRACTED ARTICLE: Evaluation of image segmentation and multi class object recognition algorithm based on machine learning

This paper mainly updates the image segmentation and multi-target recognition algorithm through the support vector machine algorithm in ML, and verifies the reliability of the new proposed algorithm through simulation experiments, and determines that the ML-based image segmentation and multi-target...

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Bibliographic Details
Published in:SN applied sciences Vol. 5; no. 5; p. 147
Main Author: Zhang, Le
Format: Journal Article
Language:English
Published: Cham Springer International Publishing 01.05.2023
Springer Nature B.V
Subjects:
Applied and Technical Physics
Chemistry/Food Science
Earth Sciences
Engineering
Environment
Materials Science
Research Article
Image segmentation
Image processing
Multi-target recognition
Artificial intelligence
Machine learning
ISSN:2523-3963, 2523-3971
Online Access:Get full text
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Summary:This paper mainly updates the image segmentation and multi-target recognition algorithm through the support vector machine algorithm in ML, and verifies the reliability of the new proposed algorithm through simulation experiments, and determines that the ML-based image segmentation and multi-target recognition algorithm can have stronger processing capability in image recognition and processing. Finally, the performance of traditional image segmentation and multi class object recognition algorithms and the image segmentation and multi class object recognition algorithms proposed in this paper were compared through experiments. The results showed that the performance of the ML based image segmentation and multi class object recognition algorithms proposed in this paper has increased by 29.1% on average in all aspects. Article Highlight This paper studies the different modes of machine learning algorithm models, and combines machine learning algorithm models with image segmentation and multi-target recognition modes, so that image segmentation and multi-target recognition algorithms can be used in image data analysis, data processing, and intelligent processing. Three aspects have better performance. In image data analysis and data processing, the advantages of machine learning models for data processing are mainly used. Through training on large amounts of data, a dedicated machine learning model for image data analysis and processing is established. The intelligent processing is reflected in the continuous improvement of this model and the further improvement of the degree of automation.
Bibliography:ObjectType-Correction/Retraction-1
SourceType-Scholarly Journals-1
content type line 14
ISSN:2523-3963
2523-3971
DOI:10.1007/s42452-023-05365-0