Open Call: What can you do with an image dataset?
We’re looking to award three £1000 commissions for the Gallery Media Wall that interrogate and bring new meaning to image datasets.
Data/Set/Match is a series of events, workshops and artists’ commissions at The Photographers’ Gallery which explore the significance of scientific image datasets.
Computer scientists are highly influential, yet often unacknowledged, creators and collectors of photographic images in the 21st Century. Technologists working in the fields of machine vision and Artificial Intelligence rely on the production and annotation of massive collections of digital images to train machines to ‘see’ and understand the world. These image datasets are typically generated by scraping images off the web (e.g. Labeled Faces in The Wild), or shot by computer scientists themselves (e.g. ATT Faces).
One of the most significant datasets today is ImageNet (2009), a visual database of over 14 million images created by a team of computer scientists led by Dr Fei-Fei Li at Stanford University. A hugely expensive and ambitious undertaking, ImageNet presents an encyclopedic image of the world, where every concept (e.g. ‘cat’, ‘bank’) has been mapped against descriptive images painstakingly annotated and collected off the web. ImageNet was the result of Dr Li’s hypothesis that computer vision would ultimately rely on the quality and scale of the training data - as opposed to the optimisation of algorithms. Its creation informed the explosion of work in the field of Artificial Intelligence and machine learning utilising neural networks.
Over the course of 12 months, Data/Set/Match aims to draw attention to these datasets and explore their creation, influence and uses. At the same time, the programme will connect the image dataset to historical photographic discourses of the archive, truth and power.
The three £1000 commissions for the Media Wall might address questions such as: What do these image collections look like and how can they be comprehended? How are they generated and created? What image of the world do they promote and what do they fail to represent? How could their use be re-imagined?
These artistic commissions could fall into one of three categories:
- the creation of a new or speculative photographic dataset, which can be released to the public and shown on the Media Wall;
- the creation of a display which re-interprets an existing contemporary or historical image dataset (beyond data visualisation); or
- the adaptation of an existing work which responds to or utilises an existing image dataset.
Deadline Wednesday 31 July 2019, 5pm
Some reference and example datasets include:
- Images in the Wild Dataset: http://vis-www.cs.umass.edu/lfw/
- Caylee Pattern dataset: http://homepages.inf.ed.ac.uk/rbf/CEILIDHDATA/
- Epic Kitchens: https://epic-kitchens.github.io/2018
- Diversity in Faces: https://www.research.ibm.com/artificial-intelligence/trusted-ai/diversity-in-faces/
- Image-net: http://www.image-net.org/
- Fruits 360: https://www.kaggle.com/moltean/fruits
- This is the Problem, the Solution, the Past and the Future: http://this-is-the-problem-the-solution-the-past-and-the-future.com/
- The USC-SIPI Image Database: http://sipi.usc.edu/database/database.php
- Caltech Camera Traps: https://beerys.github.io/CaltechCameraTraps/
- Computer Vision Test Images: https://www.cs.cmu.edu/~cil/v-images.html
- Abnormality Detection in Images: https://web.archive.org/web/20180312065423/http://paul.rutgers.edu/~babaks/abnormality_detection.html
- Kaggle’s Dataset search engine: https://www.kaggle.com/datasets
- Wikipedia Article of List of Datasets for machine learning