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: