Jadeite origin recognition based on ensemble learning

Lingling Wang; Jiahai Tu; Yuan Li; Mingyi Li

doi:10.1117/12.2674543

23 May 2023 Jadeite origin recognition based on ensemble learning

Lingling Wang, Jiahai Tu, Yuan Li, Mingyi Li

Proceedings Volume 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022); 1260435 (2023) https://doi.org/10.1117/12.2674543
Event: 2nd International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 2022, Guangzhou, China

Abstract

Most of the jade on the market now comes from Myanmar, Guatemala, and a few from Russia. The gemological properties of jadeite from different producing areas are consistent. However, in the middle-end jade market, under the same quality, the prices of Guatemalan jade and Russian jade are generally lower than those of Myanmar jade, so some illegal merchants will use Guatemalan jade to impersonate Myanmar jade. Due to the continuous improvement of jade counterfeiting technology, traditional identification methods can no longer meet the demand. In order to protect the rights and interests of consumers need to establish a rapid and effective jade origin traceability method. In this paper, through the (LA-ICP-MS) trace element dataset and the method based on weighted extreme learning machine, AdaBoost and incremental learning fusion, the jadeite discrimination model of different producing areas is established to realize the intelligent discrimination of jadeite producing areas. The recognition accuracy of integrated learning algorithm is more than 80%. Compared with the basic extreme learning machine and weighted extreme learning machine, it can be found that the classification accuracy of integrated learning algorithm is higher and more stable.

Citation Download Citation

Lingling Wang, Jiahai Tu, Yuan Li, and Mingyi Li "Jadeite origin recognition based on ensemble learning", Proc. SPIE 12604, International Conference on Computer Graphics, Artificial Intelligence, and Data Processing (ICCAID 2022), 1260435 (23 May 2023); https://doi.org/10.1117/12.2674543

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KEYWORDS

Education and training

Machine learning

Extreme learning machines

Data modeling

Neurons

Chemical elements

Decision trees

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