Museum Without Walls, Art History Without Names: Visualization Methods for Humanities and Media Studies
In the first decade of the 21st century, the researchers in the humanities and humanistic social sciences have gradually started to adopt computational and visualization tools. The majority of this work often referred as “digital humanities” has focused on textual data (e.g., literature, historical records, or social media) and spatial data (e.g., locations of people, places, or events). However, during this decade, visual media have remained outside of the new computational paradigm.
To fill this void, in 2007 I established the Software Studies Initiative at University of California, San Diego. Our first goal was to develop easy to use techniques for visualization and computational analysis of large collections of images and video suitable for researchers in media studies, the humanities, and the social sciences who do not have technical background, and to apply these techniques to progressively large media data sets. Our second goal was theoretical - to examine existing practices and assumptions of visualization and computational data analysis (thus the name “Software Studies”), and articulate new research questions enabled by humanistic computational work with “big cultural data” in general, and visual media specifically.
This chapter draws on the number of my articles written since we started the lab where I discuss history of visualization, the techniques that we developed for visualizing large sets of visual media, and their applications to various types of media. The reader is advised to consult these articles on the details of visualization methods presented and detailed analysis of their applications.
The first purpose of this chapter is to bring together the key theoretical points developed across these articles. In doing this, I also want to articulate the connections between some of the key concepts involved in visualizing media for humanities research - “artifact,” “data,” “metadata,” “feature”, “mapping,” and “remapping.” We can relate these concepts in three ways.
Firstly, we can look at these and other related concepts as series of oppositions: artifact vs. data, data vs. metadata, close reading vs. distant reading. Secondly, since the combination of these concepts correspond to fundamental conceptual steps used in various visualization methods, we can examine theoretically at each of these steps (translating from artifacts to data, adding new metadata, extracting features, mapping and remapping from data to a visual representation.)
Thirdly, we can organize our discussion in terms of these methods. For example, visualization can show the metadata about the artifacts or the actual artifacts; a researcher can use existing metadata or add new ones. The conceptual characterization of these fundamental methods is the third goal of this chapter. It organizes the methods along two conceptual dimensions. The first dimension describes what is the prime object being visualized - data or metadata. The second dimension describes the two key ways of augmenting the original data with new information used in visualization – manual annotation or automatic feature extraction.
Since my lab focused on working with visual media data sets - photography, images of art, films, cartoons, motion graphics, video games, book pages, magazine covers and pages, and so on – all the methods described will be immediately applicable to all types of visual media. However, as I will explain, not all of them will work with other types of media because of the particular properties of images and human vision.