Is a drop-in module that facilitates the creation and sharing of time-based media annotations on the Web
Knight News Challenge Prototype Grant
Knight Foundation has awarded a Prototype Grant for Media Innovation to The Media Ecology Project (MEP) and Prof. Lorenzo Torresani’s Visual Learning Group at Dartmouth, in conjunction with The Internet Archive and the VEMI Lab at The University of Maine.
“Unlocking Film Libraries for Discovery and Search” will apply existing software for algorithmic object, action, and speech recognition to a varied collection of 100 educational films held by the Internet Archive and Dartmouth Library. We will evaluate the resulting data to plan future multimodal metadata generation tools that improve video discovery and accessibility in libraries.
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
An introduction of basic notions about the challenges of computer vision. A feeling of the simple, low-level operations necessary for the next stage.
Basic image operations: scikit-image
Face-object identification + identification: dlib
Deep Learning: Keras
What is CV?
How to gain high-level understanding from digital images or videos.
It tries to resolve tasks that humans can do (Wikipedia)
Human Vision System (HVS) versus Digital Image Processing (what the computer sees)
– Jupyter system (an open-source web application that allows you to create and share documents that contain live code, equations, visualizations and explanatory text);
– perform basic image operations;
– Play with different convolutions to develop intuition.
Hands-on Part II – Deep Learning and its application
During the DH2017 conference in Montreal, I attended the ‘Computer Vision in Digital Humanities‘ workshop organized by AVinDH SIG (Special Interest Group AudioVisual material in Digital Humanities). All information about the workshop can be found here.
An abstract about the workshop was published on DH2017 Proceedings and can be found here.
This workshop focus on how computer vision can be applied within the realm of Audiovisual Materials in Digital Humanities. The workshop included: