数据驱动的阿塞拜疆平织地毯自动归属

Rashid Bakirov, Roya Taghieva, Nigar Eyvazli, Umay Mammadzada
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引用次数: 0

摘要

地毯归属是研究地毯和纺织品的重要任务,更广泛地说,是研究生产这些地毯的社区的历史和文化的重要任务。然而,这并不是一项容易的任务,通常依赖于专家的主观意见或复杂的化学或放射学分析,许多从业者通常无法获得。在这项工作中,基于机器学习和人工智能方法在不同领域的成功应用,我们提出了另一种数据驱动的地毯归因方法。基于阿塞拜疆平面编织地毯的大型数据集,我们开发了一种新颖的基于机器学习的数据驱动地毯归属系统,该系统成功地确定了它们的类型、学校和编织世纪,实现了高达98%的归属准确率。
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Data-driven Automatic Attribution of Azerbaijani Flat Woven Carpets
Carpet attribution is an important task for studying the carpets and textiles, and more generally the history and culture of the communities producing these carpets. However, this is not an easy task, often relying on experts' subjective opinion or complex chemical or radiographical analysis, often not available to many practitioners. In this work, building on the success of applying machine learning and artificial intelligence methods in different fields, we present another, data-driven approach for carpet attribution. Based on a large dataset of Azerbaijani flat woven carpets we have developed a novel machine learning based data-driven carpet attribution system, which successfully determines their types, schools and weaving century, achieving up to 98% accuracy of the attribution.
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Data-driven Automatic Attribution of Azerbaijani Flat Woven Carpets Contributions of Photometry to the 3D-digitization of Heritage Deep Level Annotation for Painter Attribution on Greek Vases utilizing Object Detection Creating a Time Machine of Future Pasts: Data Integration and Interoperability for Cross-disciplinary Research on Urban Heritage Clusters Approach to Identification of Changes from Local Surface Normal Analysis of RTI Data in Application to Cultural Heritage
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