Withdrawn as Duplicate: Distant viewing: analyzing large visual corpora

Abstract

In this article we establish a methodological and theoretical framework for the study of large collections of visual materials. Our framework, distant viewing, is distinguished from other approaches by making explicit the interpretive nature of extracting semantic metadata from images. In other words, one must 'view' visual materials before studying them. We illustrate the need for the interpretive process of viewing by simultaneously drawing on theories of visual semiotics, photography, and computer vision. Two illustrative applications of the distant viewing framework to our own research are draw upon to explicate the potential and breadth of the approach. A study of television series shows how facial detection is used to compare the role of actors within the narrative arcs across two competing series. An analysis of the Farm Security Administration-Office of War Information corpus of documentary photography is used to establish how photographic style compared and differed amongst those photographers involved with the collection. We then aim to show how our framework engages with current methodological and theoretical conversations occurring within the digital humanities.

Content-Based Exploration of Archival Images Using Neural Networks

“…Drawing on a sample of archived websites between 1999 and 2014, for example, one research study found that in 1999 the percentage of text on an average webpage was only around 22%; peaking in 2005 at 32% and declining to around 25% by 2014 [4]. Elsewhere in the digital humanities, scholars are increasingly attuned to the "visual turn": the need to not only "distantly read" material, but to "distantly view" them as well [1]; examples include applications of neural networks to analyze image collections [13]. Our own previous work has attempted to provide scholars with image access into web archives by taking advantage of object detectors based on neural networks to create collages that portray a multitude of pre-defined objects [14].…”

A 3D real-time dynamic exhibition space for I-Media-Cities audiovisual platform

“…These contents are automatically and manually enriched with metadata at the level of a single shot or image. Automatic tagging of a/v content is a very sensitive and cherished topic among digital humanities scholars [Bermeitinger et al 2019], [Arnold and Tilton 2019]. In our case the automatic analysis, performed thanks to algorithms provided by Fraunhofer Institute (https://www.fraunhofer.de/en.html), delivers shot detection, camera movement, quality assessment and object detection for 66 different objects.…”

Exhibiting Uncertainty: Visualizing Data Quality Indicators for Cultural Collections

“…Secondly, researchers can extract structured metadata from the mentioned textual object descriptions or make "intrinsic" object features explicit, by computationally extracting textual or visual features from the objects of a collection or from associated descriptions. As such, natural language processing [6,14] or computational image recognition and feature extraction techniques are frequently applied [3,[15][16][17].…”