Distant Viewing Toolkit: A Python Package for the Analysis of Visual Culture

Arnold, Taylor; Tilton, Lauren

doi:10.21105/joss.01800

Cited by 11 publications

(6 citation statements)

References 14 publications

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“…Initially developed with the goal of achieving complete image understanding (Hoiem et al, 2008), the field of CV treats images as data (Haralick & Shapiro, 1991) and generally facilitates bottom-up access to extensive image repositories (Arnold & Tilton, 2020). As such, it allows the simultaneous analysis of vast amounts of images, reducing the need for painstaking individual examination.…”

Section: Automating High-level Visual Understandingmentioning

confidence: 99%

The wicked problem of naming the intangible: Abstract concepts, binary thinking, and computer vision labels

Martinez Pandiani

2024

Future Humanities

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Delving into the intricate complexities of naming and categorizing the visual evocation of abstract concepts, this paper brings to light the limitations of relying on binary thinking to tackle these inherently intricate “wicked problems.” As computer vision applications rapidly expand, the pressing challenge of accurately labeling these abstract concepts in visual media comes into focus, necessitating a close examination of the interplay between visual data, nuanced cultural meanings, and artificial intelligence (AI). This work discusses the role these concepts play in automatic visual indexing, as well as the ways in which they expose how binary frameworks curtail technical performance and perpetuate power dynamics. To address this, the paper draws upon insights from recent cognitive neuroscience research and advocates for a more comprehensive, queer, and situated understanding of these concepts. This approach highlights the significance of humanistic and ethical perspectives in shaping the trajectory of AI development.

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Section: Automating High-level Visual Understandingmentioning

confidence: 99%

The wicked problem of naming the intangible: Abstract concepts, binary thinking, and computer vision labels

Martinez Pandiani

2024

Future Humanities

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“…Additionally, many open-source projects provide implementations of state-of-the-art computer vision algorithms applied to moving images. Some, such as the Distance Viewing Toolkit (Arnold & Tilton, 2020), are very general and handle several aspects of a moving image at once (sound, color, camera angle, etc.). Others, such as VIAN (Halter et al, 2019), allow for a "closer," more interactive distant viewing.…”

Section: Statement Of Needmentioning

confidence: 99%

KALMUS: tools for color analysis of films

Chen¹,

Faden²,

Ryan³

2021

JOSS

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KALMUS is a Python package for the computational analysis of colors in films. It provides quantitative tools to study and compare the use of film color. This package serves two purposes: (1) various ways to measure, calculate and compare a film's colors and (2) various ways to visualize a film's color. We have named the software KALMUS in homage to Natalie Kalmus (1882Kalmus ( -1965, a Technicolor Director who oversaw the color palettes of nearly 300 Hollywood feature films.

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“…This complicates our understanding of the digital object from a library perspective, but it also opens up new possibilities for managing those objects and making them discoverable. (Wevers & Smits, 2020;Arnold & Tilton, 2020) As the Collections as Data project attests, the libraries that support computational access to their collections are still few. (Padilla et al, 2019) The shift in thinking of digital materials as data sets rather than as discrete objects is not only about changing how we support users.…”

Section: From Generalizability To Accountabilitymentioning

confidence: 99%

Managing Bias When Library Collections Become Data

Coleman

2020

IJoL

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Developments in AI research have dramatically changed what we can do with data and how we can learn from data. At the same time, implementations of AI amplify the prejudices in data often framed as ‘data bias’ and ‘algorithmic bias.’ Libraries, tasked with deciding what is worth keeping, are inherently discriminatory and yet remain trusted sources of information. As libraries begin to systematically approach their collections as data, will they be able to adopt and adapt the AI-driven tools to traditional practices? Drawing on the work of the AI initiative within Stanford Libraries, the Fantastic Futures conference on AI for libraries, archives, and museums, and recent scholarship on data bias and algorithmic bias, this article encourages libraries to engage critically with AI and help shape applications of the technology to reflect the ethos of libraries for the benefit of libraries themselves and the patrons they serve. A brief examination of two core concepts in machine learning, generalization and unstructured data, provides points of comparison to library practices in order to uncover the theoretical assumptions driving the different domains. The comparison also offers a point of entry for libraries to adopt machine learning methods on their own terms.

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Distant Viewing Toolkit: A Python Package for the Analysis of Visual Culture

Cited by 11 publications

References 14 publications

The wicked problem of naming the intangible: Abstract concepts, binary thinking, and computer vision labels

The wicked problem of naming the intangible: Abstract concepts, binary thinking, and computer vision labels

KALMUS: tools for color analysis of films

Managing Bias When Library Collections Become Data

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