Research on the Image Annotation Technology for Product Quality and Safety Inspection Data

Autor:	Yingcheng Xu, Xiuli Ning, Xiaowei Lu, Ying Li
Rok vydání:	2020
Předmět:	History Automatic image annotation Database Computer science media_common.quotation_subject Quality (business) Product (category theory) computer.software_genre computer Computer Science Applications Education media_common
Zdroj:	Journal of Physics: Conference Series. 1487:012020
ISSN:	1742-6596 1742-6588
DOI:	10.1088/1742-6596/1487/1/012020
Popis:	In recent years, the vicious events about quality and safety in China have continued to bring serious impacts on people’s lives and property. The effective analysis and processing of product quality and safety inspection data will provide intellectual support for the overall improvement in product quality, and the effective control of prominent quality and safety problems in China. Aiming at the phenomenon that there is a large amount of image information in the quality detection data, this paper proposed an image annotation technology based on big data fusion, conducted weight fusion to image similarity and image user similarity, calculated the total similarity of images, and made denoising treatment. The experimental results showed that the method proposed in this study could annotate the quality test data well.
Databáze:	OpenAIRE
Externí odkaz:	https://explore.openaire.eu/search/publication?articleId=doi_________::d95a16109416da13e054412e4c1cc45f https://doi.org/10.1088/1742-6596/1487/1/012020 Zobrazit plný text záznamu