EAGER: Digging into Image Data to Answer Authorship Related Questions

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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.