基于深度学习的彩陶几何纹饰交互标注

IF 1 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
S. Lengauer, Peter Houska, R. Preiner, E. Trinkl, S. Karl, I. Sipiran, Benjamin Bustos, T. Schreck
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引用次数: 0

摘要

在希腊艺术中,公元前900年至公元前700年这一时期被称为几何时期,因为这一时期彩陶表面出现了典型的简单几何状装饰。独特的几何图案是特定时期、地区、作坊和画家的典型特征,也是考古任务的重要线索,比如年代和归属。迄今为止,这些分析大多是在信息技术的支持下进行的。一件人工制品的纹饰的基本元素通常可以分为一组可区分的纹饰,这些纹饰也以类似的方式出现在其他物品上。虽然在特定主题的出版物中给出了已知模式类的分类,但是从对象描述中自动检测和分类表面模式提出了一个不小的挑战。我们的长期目标是使用专门设计和训练的神经网络来提供这种分类功能。然而,这需要大量标记的训练数据,而这一点在这个领域上下文中并不存在。在这项工作中,我们提出了一个有效的标注系统,该系统允许领域专家交互式地分割和标记数字化容器表面的部分。这些用户输入不断反馈到卷积神经网络(CNN),从而能够以越来越高的精度预测给定表面积的模式类别。我们的工作为大型表面图案集合的全自动分类和分析铺平了道路,在合适的视觉分析技术的帮助下,可以回答图案可变性或随时间变化等研究问题。虽然我们提出的注释管道的能力在两个具有几何时期特征的希腊陶器文物的例子中得到了证明,但所提出的方法可以很容易地用于任何其他时间顺序时期的图案以及印花图案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Interactive annotation of geometric ornamentation on painted pottery assisted by deep learning
Abstract In Greek art, the phase from 900 to 700 BCE is referred to as the Geometric period due to the characteristically simple geometry-like ornamentations appearing on painted pottery surfaces during this era. Distinctive geometric patterns are typical for specific periods, regions, workshops as well as painters and are an important cue for archaeological tasks, such as dating and attribution. To date, these analyses are mostly conducted with the support of information technology. The primitives of an artefact’s ornamentation can be generally classified into a set of distinguishable pattern classes, which also appear in a similar fashion on other objects. Although a taxonomy of known pattern classes is given in subject-specific publications, the automatic detection and classification of surface patterns from object depictions poses a non-trivial challenge. Our long-term goal is to provide this classification functionality using a specifically designed and trained neural network. This, however, requires a large amount of labelled training data, which at this point does not exist for this domain context. In this work, we propose an effective annotation system, which allows a domain expert to interactively segment and label parts of digitized vessel surfaces. These user inputs are constantly fed back to a Convolutional Neural Network (CNN), enabling the prediction of pattern classes for a given surface area with ever increasing precision. Our work paves the way for a fully automatic classification and analysis of large surface pattern collections, which, with the help of suitable visual analysis techniques, can answer research questions like pattern variability or change over time. While the capability of our proposed annotation pipeline is demonstrated at the example of two characteristic Greek pottery artefacts from the Geometric period, the proposed methods can be readily adopted for the patternation in any other chronological periods as well as for stamped motifs.
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来源期刊
IT-Information Technology
IT-Information Technology COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
3.80
自引率
0.00%
发文量
29
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