With the advent of more technology to help add metadata to digital assets, it would good to review a few tagging options available aside from what may already be done within an organization.
Some DAM systems do not make it easy to apply a controlled vocabulary, taxonomy or any list for users to pick from when it comes to applying tags in the process. Keep in mind tags are just one form (and often one field) of metadata out of many possible options.
What is Tagging?
The act of applying tags (keywords or key phrases) is tagging. We are not talking about vandalizing walls nor subway trains with "artwork." We are however making a mark in an organization or community by making its digital assets more searchable, more findable (within finite results) and possibly better monetized.
If you can search for digital assets, you should find the relevant digital assets you need and these digital assets could easily be distributed if this happens. If a client can not find the digital asset they need, they can not buy/license/use this digital asset. A number of photo agencies have found this out the hard way after some time, but this effort extends to all media including audio, video, text, graphics and photos. A few large sporting organizations have massive archives of their sports history waiting to be tagged. How could this be done for them as well as your organization?
What is Auto-tagging?
Auto-tagging is tagging (adding metadata) in an automated fashion via computer with complex algorithms. Often, these algorithms work by analyzing the content (often visual images) to match shapes and patterns such as faces.
More tools now have facial recognition based on the position of an eye, nose and mouth. Advanced facial recognition also looks at the forehead, cheeks, chin and sometimes ears. If you apply the name of someone to some images of this person, the software will do the rest with reasonable accuracy.
(Just make sure you do not smile. Kind of like with your passport and driver's license. I dare you to smile at airport customs or the motor vehicle administrator's office while waiting in line and see how long that smile stays on your face. We know that smile will not stay long on anyone's face unless you want to be profiled.)
Beyond faces, common shapes and patterns yield mixed results dependent on the image content, quality of the image, resolution and focus.
At a meetup, I spoke with someone who works for a company which offered auto-tagging. A few large social networks may be using these services as well.
I have reviewed some DAM and MAM systems with similar auto-tagging tools, but I was not amazed with the results (yet). When auto-tagging was used on images, results came back as trees for a photograph of grass (both green and vertical, but not close enough). When a photo of strawberry was auto-tagged, the results returned with cherry (both red, round and fruit, but the texture is visibly different between the two).
One service I did see was auto-tagging for video which did quite well. I was asked to review this tool. As a test to have them prove themselves, I sent them an early silent film posted online and some music videos to see what the tool could do. The quality issues of the silent film as well as the abstract nature of the music video would be a challenge.
The test yielded very good results based on the tool analyzing what patterns and shapes could be found frame by frame. If the pattern appeared within a number of video frames within a given period of time, the tool produced tags for this pattern.
Crowdsourcing Work Done for You
At a recent lecture, I listened to a few experts explaining some new services where there are some mechanical turks (people doing repetitive micro-tasks remotely) doing some tagging. There now some new players on the field of crowdsourcing metadata. A few of these services are very big, while most are still small. Many have big potential.
Many of these services are cloud-based now, while a few are in-house installs which could be integrated with other systems. Most of these groups are using global resources. Some of these services are gamified just like the early beginning of some university projects to help a community tag their digital assets and get a high score as another personal bonus.
There are some news reports about these services as well as word of mouth within some communities of some archivist groups, digital asset management groups, humanities groups, information management groups, librarians and metadata management groups.
Micro-tasks for Micro-payments with Error Checking Process
These crowdsourced micro-tasks (tag a few images) are often paying individuals who are nationally or internationally distributed around the globe just a few pennies per task (tag an image with N number of keywords or key phrases based on a controlled vocabulary).
The question arises why would anyone really care to apply relevant tags in an accurate manner for payment of just a few pennies per image?