用机器学习自动分级祖母绿，准确率达98%。
Machine learning applied to emerald gemstone grading: framework proposal and creation of a public dataset
- 结合图像处理与机器学习，全自动完成祖母绿分级
- 98%准确率，优于传统深度学习方法
- 首次公开192张祖母绿图像及预处理特征数据集
祖母绿分级目前依赖宝石学家的主观判断，常使用参考石进行视觉比对，导致结果不一致。本文提出一个完整框架，涵盖图像采集到最终分级，仅需人工将石头放入成像腔室即可实现全流程自动化，消除人为主观性。这是首个将机器学习与图像处理技术结合用于祖母绿分级的研究。所提框架在192张祖母绿图像上达到98%的分类准确率，优于现有深度学习方法。同时，研究创建并公开了该数据集，包含192张祖母绿图像及其提取的预处理特征。
原文摘要 · Abstract (English)
The grading of gemstones is currently a manual procedure performed by gemologists. A popular approach uses reference stones, where those are visually inspected by specialists that decide which one of the available reference stone is the most similar to the inspected stone. This procedure is very subjective as different specialists may end up with different grading choices. This work proposes a complete framework that entails the image acquisition and goes up to the final stone categorization. The proposal is able to automate the entire process apart from including the stone in the created chamber for the image acquisition. It discards the subjective decisions made by specialists. This is the first work to propose a machine learning approach coupled with image processing techniques for emerald grading. The proposed framework achieves 98% of accuracy (correctly categorized stones), outperforming a deep learning approach. Furthermore, we also create and publish the used dataset that contains 192 images of emerald stones along with their extracted and pre-processed features.
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