We recently expanded our Enterprise Tagging Platform to do near real-time content filtering of images, blogs, comments and advertisements. To give you a clear understanding of how this service can be useful I’ve developed a simple use case:

Use Case 1: Allowing for custom display ads

Background: On October 12, 2008 MySpace launched the self-serve advertising platform myAds which allows advertisers to create and run display ads on MySpace’s website. The advertisers can choose between pre-fabricated display ad templates or submit their own custom templates and then target those ads to specific demographics on MySpace. You can read about it in detail on TechCrunch: myAds Overview

Problem: Many companies allow for pre-fabricated templates to be used as the basis for display ads, profile backgrounds and photo album themes, but how can they possibly give millions of users the option to submit their own templates without exposing other users and advertisers to mature or inappropriate content?

Solution: Integrating Tagcow’s content filtering service into the “user generated template” submission and approval process. Once the custom template is submitted it would be pushed to our platform where it could be filtered against our client’s acceptable content guidelines and then returned to the client’s system with a status of approved or rejected.

Potential Users: Online Newspaper Sites, User Generated Art Sites, Photo Sharing Sites, Social Networking & Dating Sites and Online Media Companies implementing user generated content driven marketing campaigns.

Token diagram:

We continue to make significant progress on-boarding companies to our Enterprise Tagging Platform. Last night I was doing a quick quality check on a job that was submitted for a well known museum and was completely floored by the quality of work that is being submitted by our workers. This enterprise tagging job had some serious challenges; first it requires a minimum of 10 tags per image and second a lot of the images are of simple objects from the museum (some of which are not very exciting). I tried tagging some of the images myself and could barely do it and definitely couldn’t do a very good job of it. With that I was a little afraid that we wouldn’t be able to produce quality results cost effectively but boy was I wrong. Below is an example image and subsequent tags submitted by one of our workers.  It’s not mind blowing, but it’s exactly what is called for (quality and cost effective).  

Oh yeah…and the main point of this post is to send out a huge THANK YOU to all of our workers. Without you we’re nothing. You enable everything we do. Tens of thousands of human taggers enabling enterprise tagging of millions and millions of images! Sweet!

Tags: abstract, industrial art, nonrepresentational, metal, sculpture, angular, geometric sharp, surfaces, steel, rust, weld, welds, welding, welded, punch hole

I can’t help but include this tagging example from our consumer service which reiterates the quality tagging done by our workers.

Tags: Michael Droz, mjdchild, 1980s, hungry hippos, batman, bop bag, crayola crayons, mickey mouse, candy, santa, christmas, gifts

It is commonly known that search engines are currently incapable to identifying objects or content within the image and although there has been much work recently for computers to perform this work, the technology is not quite there. This problem leads to one of the most common questions we see pertaining to the value of image or photo tagging.  The answer to this question depends on the solution you are seeking. General users with personal image collections have different needs than those of enterprise customers but the problem (and solution) is the same.

Personal

Personal or power users typically have growing collections of images that are stored in a flat directory structure.  Unless they are disciplined or exact about how those images are stored and cataloged, images are stored by date and maybe by event.  However, most users don’t go so far as to change the image naming structure so even when they are organized by date, there is no detail on what is in the photo.  What they end up with is a bunch of images with file names like “DSC001248.jpg.”  Not very descriptive to say the least!  What this means to the user is if they want to find all pictures of their daughter, they must rely on their memory or spend time looking through thumbnail images to find images they are searching for.  While this may be a viable solution, it is not very practical as their collection grows from 100’s to 1000’s of digital images over time.

Enterprise

Enterprise customers have a similar problem except theirs can cost them money.  Enterprises have large collections of images that are stored in repositories.  Although they tend to do a better job at renaming their images with names that relate to the content in the image, it is not practical to list out every detail in the image and include that in the file name.  Alternatively, some enterprise customers will create elaborate directory structures to account for the insufficiency in identifying image content.  However, as the repositories grow, enterprise customers are faced with an ever growing collection of images that are essentially lost or undiscovered because of the shear number of images in the repository.  This can cost a company sales especially if a customer is unable to find a particular piece of art, for example.

So what can they do.  Tagcow was started to address this issue.  Instead of waiting for the technology to arrive to discover the content within an image, we approached the problem from a human point of view.  Humans are uniquely qualified to identify the content in an image and will, for the foreseeable future, be able to add tags, such as human emotions, that computers will find difficult to ever tag.  That said, image tags will empower users, personal and enterprise, to search their images or photos in a way never before possible: by content.