DH2017 – Computer Vision in DH workshop (Keynote)

Robots Reading Vogue Project

A keynote by Lindsay King & Peter Leonard (Yale University) on “Processing Pixels: Towards Visual Culture Computation”.

SLIDES HERE

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.

Project Robots Reading Vogue project at Digital Humanities Lab Yale University Library

1) The project:

  • 121 yrs of Vogue (2,700 covers, 400,000 pages, 6 TB of data). First experiments: N-Grams, topic modeling.
  • Humans are better at seeing “distant vision” (images) patterns with their own eyes than  “distant reading” (text)
  • A simple layout interface of covers by month and year reveals patterns about Vogue’s seasonal patterns
  • The interface is not technically difficult do implement
  • Does not use computer vision for analysis

2) Image analysis in RRV (sorting covers by color to enable browsing)

    • Media visualization (Manovich) to show saturation and hue by month. Result: differences by the season of the year. Tool used:  ImagePlot
    • “The average color problem”. Solutions:
    • Slice histograms: Visualization Peter showed.

The slice histograms give us a zoomed-out view unlike any other visualizations we’ve tried. We think of them as “visual fingerprints” that capture a macroscopic view of how the covers of Vogue changed through time.
  • “Face detection is kinda of a hot topic people talk about but I only think it is of use when it is combined with other techniques’ see e.g. face detection within 

    3. Experiment Face Detection + geography 

  •  Photogrammer
Face Detection + Geography
  • Code on Github
  • Idea: Place image as thumbnail in a map
  • Face Detection + composition
Face Detection + composition

4. Visual Similarity 

  • 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.
  • 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
  • ARCHIVE-VISION Github code here
  • Peter refers to paper Benoit presented in Krakow.

5. Final thoughts and next steps

  • Towards Visual Cultures Computation
  • NNs are “indescribable”… but we can dig in to look at pixels that contribute to classifications: http://cs231n.github.io/understanding-cnn/
  • The Digital Humanities Lab at Yale University Library is currently working with as image dataset from YALE library through Deep Learning approach to detect visual similarities.
  • This project is called Neural Neighbors and there is a live demo of neural network visual similarity on 19thC photos
Neural Neighbors seeks to show visual similarities in 80,000 19th Century photographs
  • The idea is to combine signal from pixels with signal from text
  • Question: how to organize this logistically?
  • Consider intrinsic metadata of available collections
  • Approaches to handling copyright licensing restrictions (perpetual license and transformative use)
  • Increase the number of open image collections available: museums, governments collections, social media
  • Computer science departments working on computer vision with training datasets.