EAGER: Digging into Image Data to Answer Authorship Related Questions

本课题设计图像分析算法，提取图像显著特征，根据特征的相似度对图像进行分组，根据先验知识对图像进行分类，并优化算法步骤和参数。研究小组将共同开发的算法应用于三个图像集合；并报告所有图像集合的准确性和计算需求。研究活动通过基于数据集的艺术、科学和技术问题来解决个人和集体作者的问题，并开发相应的图像分析，从而在计算上可扩展和准确的数据驱动下发现显著和区分特征。更具体地说，该项目(a)促进针对作者问题的创新图像分析的发展和部署，并应用于大规模数据分析；(b)促进人文科学、计算机科学和信息科学学者之间的跨学科合作；(c)促进国际和国内合作；(d)在一组大型的、多样化的数字集合上获得了独特的准确性和计算可扩展性的发现，这些发现通过网格提供给来自互补学科的重要研究人员，他们热衷于相互学习。该项目是国际、多机构和多学科努力的一部分，共同探索三种不同但在某些方面互补的数字数据集的作者身份：15世纪手稿、17世纪和18世纪的地图以及19世纪和20世纪的被子。

This project designs image analysis algorithms that extract salient image features, group images based on similarity of these features, classify groups according to a priori knowledge, and optimize algorithmic steps and parameters. The research team applies the algorithms jointly developed to the three collections of images; and reports accuracy and computational requirements over all of the image collections. The research activities address problems of individual and collective authorship via artistic, scientific and technological questions based on the datasets, and developing the corresponding image analyses leading to computationally scalable and accurate data-driven discoveries of salient and discriminating characteristics. More specifically, the project, (a) promotes the development and deployment of innovative image analyses targeting the problem of authorship and applied to large-scale data analysis; (b) fosters interdisciplinary collaboration among scholars in the humanities, computer sciences, and information sciences; (c) promotes international and domestic collaborations; and (d) leads to unique accuracy and computational scalability findings over a set of large, diverse digital collections made available over the grid to a significant body of researchers from complementary disciplines keen to learn from each other. The project is a part of international, multi-institutional and multi-disciplinary efforts that jointly explore authorship across three distinct but in some respects complementary digital dataset collections: 15th-century manuscripts, 17th- and 18th-century maps and 19th- and 20th-century quilts.