Published on 12th October, this map represents every building in the US by a black speck, reflecting the built legacy of the United States.
The dataset was publicly released by Microsoft early this year. The company’s computer engineers trained a neural network to analyze satellite imagery and then to trace the shapes of buildings across the country.
The map reveals patterns in the arrangements of buildings. Traditional road maps highlight streets and highways; here they show up as a linear absence. As a result, “… you can read history in the transition from curving, paved-over cow paths in old downtowns to suburban sprawl; you can detect signals of wealth and poverty, sometimes almost next door to each other.”.
In the south of New Orleans, it’s possible to notice the layout of buildings along a narrow spit of land on either side of a Louisiana bayou, which may reflect the imprint of the region’s history under France: “… “long lot” development, which stretched skinny holdings laterally away from important waterways. Geography shapes settlement, but culture does, as well.”
The Wikidata extraction contains more than 133 866 artworks (September 2018) including 66 271 with HD image. This extraction is regularly automatically updated from Wikidata on the basis of the nature of the items and corresponds to visual artworks such as paintings, photographs, prints, illuminated manuscripts and much more.
Searches can be made by free or indexed search through a user interface. Results are displayed by chronological order with thumbnails. Links on thumbnails open a viewer with the image hosted on Wikimedia Commons.
It is possible to filter the results by type (painting, sculpture, print…) or to specify a period as a criterion.
By default, without criteria, a random selection is displayed. Besides with the Cosmos interface, it is possible to discover the artworks by indexation (par type d’œuvre, creator, movement, genre, collection…).
For each resulting image, the interface displays the title, the creator(s) and the collection or the location where the artwork is maintained. These information are on Wikidata, a free, collaborative, multilingual, secondary database, collecting structured data to provide support for Wikipedia, Wikimedia Commons, the other wikis of the Wikimedia movement, and to anyone in the world.
Additional descriptors are date or period, nature of work, material used, inventory number, movement, genre, depicts, main subject, and so on. A full list of descriptors is mentioned here.
This presentation is about Decomposing Bodies, a large-scale, lab-based, digital humanities project housed in the Visual Media Workshop at the University of Pittsburgh that is examining the system of criminal identification introduced in France in the late 19th century by Alphonse Bertillon.
Data: System of criminal identification from American prisoners from Ohio.
Tool: OpenFace. Free and open source face recognition with deep neural networks.
Goal: An end-to-end system for extracting handwritten text and numbers from scanned Bertillon cards in a semi-automated fashion and also the ability to browse through the original data and generated metadata using a web interface.
Mechanical Turk: we need to talk about it”: consider Mechanical Turk if public domain data and task is easy.
Findings: Humans deal very well with understanding discrepancies. We should not ask the computer to find these discrepancies to us, but we should build visualizations that allow us to visually compare images and identify de similarities and discrepancies.
2) Distant Viewing TV (Taylor Arnold and Lauren Tilton, University of Richmond)
Distant Viewing TV applies computational methods to the study of television series, utilizing and developing cutting-edge techniques in computer vision to analyze moving image culture on a large scale.
This presentation related the University of Oxford’s Visual Geometry Group’s experience in making images computationally addressable for humanities research.
The Visual Geometry Group has built a number of systems for humanists, variously implementing (i) visual search, in which an image is made retrievable; (ii) comparison, which assists the discovery of similarity and difference; (iii) classification, which applies a descriptive vocabulary to images; and (iv) annotation, in which images are further described for both computational and offline analysis
Idea: Visual Search for the Era of Big Data is a large research project based in the Department of Engineering Science, University of Oxford. It is funded by the EPSRC (Engineering and Physical Sciences Research Council), and will run from 2015 – 2020.
Objectives: to carry out fundamental research to develop next generation computer vision methods that are able to analyse, describe and search image and video content with human-like capabilities. To transfer these methods to industry and to other academic disciplines (such as Archaeology, Art, Geology, Medicine, Plant sciences and Zoology)
This is a technical demo of the large-scale on-the-fly web search technologies which are under development in the Oxford University Visual Geometry Group, using data provided by BBC R&D comprising over five years of prime-time news broadcasts from six channels. The demo consists of three different components, which can be used to query the dataset on-the-fly for three different query types: object search, image search and people search.
The objective of this research is to find objects in paintings by learning classifiers from photographs on the internet. There is a live demo that allows a user to search for an object of their choosing (such as “baby”, “bird”, or “dog, for example) in a dataset of over 200,000 paintings, in a matter of seconds.
It allows computers to recognize objects in images, what is distinctive about our work is that we also recover the 2D outline of the object. Currently, the project has trained this model to recognize 20 classes. The demo allows the user to test our algorithm on their images.
Abstract: This talk will focus on an array of algorithmic image analysis techniques, from simple to cutting-edge, on materials ranging from 19th century photography to 20th century fashion magazines. We’ll consider colormetrics, hue extraction, facial detection, and neural network-based visual similarity. We’ll also consider the opportunities and challenges of obtaining and working with large-scale image collections.
What if we could search for pictures that are visually similar to a given image
Neural networks approach
Demo of Visual Similarity experiment:
In the main interface, you select an image and it shows its closest neighbors.
Other related works on Visual Similarities:
John Resig’s Ukiyo-e (Japenese woodblock prints project). Article: Resig, John. “Aggregating and Analyzing Digitized Japanese Woodblock Prints.” Japanese Association of Digital Humanities conference, 2013.
John Resig’s TinEye MatchEngine (Finds duplicate, modified and even derivative images in your image collection).
Carl Stahmer – Arch Vision (Early English Broadside / Ballad Impression Archive)
Article: Stahmer, Carl. (2014). “Arch-V: A platform for image-based search and retrieval of digital archives.” Digital Humanities 2014: Conference Abstracts