Departamento de Ingeniería Informática, Escuela Politécnica Superior, Universidad de Burgos, Burgos, Spain.
CaSEs - Culture and Socio-Ecological Systems Research Group, Department of Humanities, Pompeu Fabra University, Barcelona, Spain.
Microsc Microanal. 2020 Dec;26(6):1158-1167. doi: 10.1017/S1431927620024629.
Phytoliths can be an important source of information related to environmental and climatic change, as well as to ancient plant use by humans, particularly within the disciplines of paleoecology and archaeology. Currently, phytolith identification and categorization is performed manually by researchers, a time-consuming task liable to misclassifications. The automated classification of phytoliths would allow the standardization of identification processes, avoiding possible biases related to the classification capability of researchers. This paper presents a comparative analysis of six classification methods, using digitized microscopic images to examine the efficacy of different quantitative approaches for characterizing phytoliths. A comprehensive experiment performed on images of 429 phytoliths demonstrated that the automatic phytolith classification is a promising area of research that will help researchers to invest time more efficiently and improve their recognition accuracy rate.
植硅体可以为环境和气候变化以及人类对古代植物的利用提供重要的信息来源，尤其是在古生态学和考古学领域。目前，植硅体的识别和分类是由研究人员手动完成的，这是一项耗时且容易出现分类错误的任务。植硅体的自动分类将允许鉴定过程标准化，避免与研究人员的分类能力相关的潜在偏差。本文对六种分类方法进行了比较分析，使用数字化的微观图像来检查不同定量方法在描述植硅体特征方面的有效性。对 429 个植硅体的图像进行的综合实验表明，自动植硅体分类是一个很有前景的研究领域，它将帮助研究人员更有效地投入时间并提高他们的识别准确率。
