Hyperimage Index

Rendering Research on Algorithmic Image Systems

Authors

  • Sheung Yiu

DOI:

https://doi.org/10.7146/aprja.v11i1.134309

Keywords:

collective indexing, network, scale, algorithmic image, database aesthetics, distributed curating

Abstract

Image has gone hyper, can research catch up? This essay proposes collective indexing as an alternative to academic publishing for rendering research on fast-changing and larger-than-human subjects such as algorithmic images. Following the introduction of notions of network and scale in my research, the essay articulates the value of collective indexing while mapping out contemporary examples. Collective indexing produces new ways of knowledge making and community building, as well as new forms of research aesthetics apt for addressing the distributed nature of algorithmic image systems.

Author Biography

Sheung Yiu

Sheung Yiu is a Hong-Kong-born, image-centered artist and research-er, based in Helsinki. His artwork explores the act of seeing through algorithmic models and sense-making through networks of images. His research interests concern the increasing complexity and agency of computer-generated imagery (CGI) in contemporary digital culture. He looks at photography through the lens of new media, scales, and network thinking. Yiu’s work takes the form of photography, videos, photo-objects, exhibition installations, and bookmaking.

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Published

2022-10-18

Issue

Vol. 11 No. 1 (2022): Rendering Research

Section

Articles

License

Copyright (c) 2022 Sheung Yiu

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