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