Our worker platform will augment our Mechanical Turk implementation by allowing 3rd party developers and worker networks to plug directly into our platform and request/process image and photo tagging work. There’s already 1 major web 2.0 site that has implemented our API and is looking to have their users tag our images as a way to earn credits on for their system…oh the implications!
Automatic Image and Photo Tagging
When we first launched our image and photo tagging service in late March of this year (2008) a lot of people speculated on how we had “solved” the image and photo tagging problem. Some thought we had developed a breakthrough in automatic image and photo tagging while others more knowledgeable about the problems facing automatic image and photo tagging chirped about how we must be using humans to do our tagging. Well the latter were correct; we were in fact leverage humans to drive a great percentage of our tags. What I found most interesting about this debate was that no one considered that we would employ both humans and the “best of breed” facial and object recognition software to solve the problem – which is exactly what we’re doing. So for those who weren’t paying attention here’s a quick glimpse into where we started, where we are now and where we are headed.
March 2008: 90% human tagging / 10% automatic image and photo tagging (knowledge abstraction technology)
May 2008: 80% human tagging / 20% automatic image and photo tagging (knowledge abstraction technology and inference tagging technology)
July 2008: Testing partnerships with the leading facial and object recognition researchers and corporations.
August 2008: Implementation of facial recognition software to augment our other tagging technologies. We anticipate that this will reduce human tagging of “people” to 50%.
September 2008: Implementation of object recognition software to augment our other tagging technologies. We anticipate that this will reduce human tagging of “objects” to 75%.
October 2008: 65% human tagging / 35% automatic image and photo tagging (knowledge abstraction technology, inference tagging technology, facial recognition software and object recognition software)
Our goal is to get to a 50/50 split by mid 2009. Further improvements in automatic image and photo tagging will be offset by advanced human tagging, so we anticipate the 50/50 split to continue on for the foreseeable future.
We started with humans because we aren’t in business to innovate – we’re in business to completely solve problems. Human taggers solve the image tagging problem – period! Now, you may not like it and you may not be willing to pay for it, but it does solve the problem. Once the problem was solved we were left with two easy business problems; how to reduce costs and how to improve quality. Reducing costs will be achieved through implementing proprietary and licensed automatic tagging software and improved quality will be achieved through the combination of proprietary software and advanced human tagging.
Michael Droz, Chief Business Architect, mdroz@tagcow.com, (888) 860-3024
A note on image tagging templates…
Our enterprise image tagging service has taught us a lot about how we should extend our consumer service. When a large e-commerce company engages us in a significant (60,000 – 10 million) image tagging job the challenge is in understanding their taxonomy and how to abstract that into a template that can drive quality image tagging results. We’ve become really good at this and have recently realized that while each of our enterprise customer seems to have a different reason for tagging their images and a unique taxonomy the concept of “image tagging templates” extends to our consumer service. In fact we can improve our image tagging quality by leaps and bounds by creating a handful of templates that our consumer image tagging clients can use – templates such as vacation, birthday, Christmas, wedding, Halloween and bar mitzvah.
Once we’ve implement the standard “image tagging templates” the next leap in quality and flexibility will come in the form of a “image tagging template wizard” that will allow clients to create and share their own templates.
BTW – If you’re interested in our Enterprise Image Tagging solution please call or e-mail me. You need this service if you are currently employing your own taggers or if you’re working on an SEO campaign and are just starting to think about how you might tag thousands or millions of images. We can do your tagging job faster and cheaper than anyone else.
Michael Droz, Chief Business Architect, mdroz@tagcow.com, (888) 860-3024
We are excited to announce the release of the TagCow.com Worker API Beta!
The TagCow.com Worker API allows networks of workers to retrieve work items and submit the results to the TagCow.com system.
If you have a network of workers that you think can excel at tagging photos and providing professional meta data services for our enterprize customers, please email businessdevelopment@tagcow.com to setup a trial.