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Image Tagging at the Associated Press

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AP's project to apply additional metadata to our images, using custom image recognition technology.

Presented to the IPTC on October 16th 2018

Publicada em: Tecnologia
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Image Tagging at the Associated Press

  1. 1. Stuart Myles Director of Information Management at the Associated Press
  2. 2. What is Image Recognition? • Technology to recognize people, places, things or emotions in an image • Available as APIs, as well as open source software • Image recognition involves building a model to identify a set of topics • Topics can be anything you want - baseball, happy faces, drug use, war… • Requires lots of example images, so the software works out what patterns to look for • Consumers of image recognition services are often stock agencies • Off the shelf models are therefore available for concepts like • Graphic/NSFW • Celebrities • Emotion • General keywords – wedding, food • Many commercial companies also offer to create custom models – for a higher fee @smyles
  3. 3. Improve Search and Auto-Publishing • Improve search experience • In AP portals and in customer CMSes • Keywords to match more queries and so surface more content • Filters to narrow results to more relevant content • Simplify auto publishing • AP customers have fewer – or even no – editors to manually review content before publishing • Filters let customers fine-tune saved searches • Eliminate customer need to manually identify graphic content @smyles
  4. 4. AP Images Metadata • AP handles 3,000 – 4,000 images a day • Digitize 700 – 800 photos a month • AP Images has about 34 million photos • AP already applies metadata to images • Manually by photographers and editors • Mapped from third party feeds • Automatically based on photo text – such as caption – via AP’s tagging service • We manually keyword some archive images @smyles
  5. 5. Early Days: First Half of 2017 • Early in 2017, we evaluated leading vendors • None offered custom tagging • Disappointing results • Too many keywords that do not apply to image • Inaccurate keywords scored with high confidence • Some were strong for stock images, not so good for news • Others were too generic @smyles
  6. 6. High confidence: Sunglasses Woman Man Low confidence: Finger Hand And notice: watch
  7. 7. High confidence: Bat Batter Baseball Softball And notice: watch
  8. 8. Technology Evolves: Second Half of 2017 • We evaluated open source software • Future option, but the software isn’t mature yet • Later in 2017, vendors upgraded their offerings • Most added custom tagging • Working with business and sales, we designed a new image taxonomy • Sports actions, NSFW filters, emotions, and image attributes • Complements the existing news taxonomy we apply to text content @smyles
  9. 9. A Hybrid Approach: Out of the Box + Custom • Use out-of-the-box tagging for most concepts • Train custom tagger for any concepts not covered (or covered well) by OOB tagger • Find example images for each concept e.g. “tackle” - anywhere from 500 to 5,000 examples per concept • Test the tagger to make sure it is accurate • Feed the tagger more examples where it underperforms • Proof image tagging in Production • High confidence tags accepted as-is • Ignore low confidence tags • Medium confidence tags reviewed by Editorial @smyles
  10. 10. Train and Test Management • We assembled training sets that we shared with the partner • And we held back test images • Testing for accuracy • Precision • Recall
  11. 11. Things We Learnt • Assembling test and train sets is arduous • But also where most of the value lies • Some concepts are difficult to distinguish • Dawn / Dusk, Happy / Jubilant • Perceived concepts are different than text subjects • May require some reorganization of our taxonomy and how we represent it @smyles
  12. 12. Thank you! Questions? @smyles

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