EAGER: Digging into Image Data to Answer Authorship Related Questions

本项目设计图像分析算法，提取显著的图像特征，根据这些特征的相似性对图像进行分组，根据先验知识对组进行分类，并优化算法步骤和参数。研究小组将联合开发的算法应用于三个图像集;并报告了所有图像集的准确性和计算要求。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，从而导致计算可扩展和准确的数据驱动的显着和区别特征的发现。 更具体地说，该项目：（a）促进针对作者身份问题的创新图像分析的开发和部署，并将其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）促进国际和国内合作。和（d）导致独特的准确性和计算的可扩展性的结果，在一组大的，不同的数字收集提供了在网格上的一个重要机构的研究人员从互补的学科热衷于相互学习。该项目是国际，多机构和多学科努力的一部分，共同探索三个不同但在某些方面互补的数字数据集收藏的作者身份：15世纪手稿，17和18世纪地图以及19和20世纪棉被。

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.