Tagging.tech presents an audio interview with Kevin Townsend about keywording services

Listen and subscribe to Tagging.tech on Apple Podcasts, AudioBoom, CastBox, Google Play, RadioPublic or TuneIn.

Keywording Now: Practical Advice on using Image Recognition and Keywording Services

Now available

keywordingnow.com

Transcript:

Henrik de Gyor:  This is Tagging.tech. I’m Henrik de Gyor. Today, I’m speaking with Kevin Townsend. Kevin, how are you?

Kevin Townsend:  Good, thank you.

Henrik:  Kevin, who are you and what do you do?

Kevin:  I’m the CEO and Managing Director for a company called KeedUp. What we do is keywording, but also adding other metadata, fixing images, image flow services; a whole heap of things, but keywording and metadata is really the core of what we do.

What makes us a little bit different to maybe some other keywording companies is that we started out from a basis of being involved in the industry as a syndicator/image seller. We were like a photo agency, photo representative, like many of our customers ‑‑ in fact almost all of our customers.

As a result, we’ve developed services in a somewhat different way. For instance, we operate 24 hours a day, seven days a week. We do celebrity as well as stock. Everybody that works for us pretty much is working in an office. There’s no piecework. Almost all of our staff are university graduates.

Henrik:  Kevin, what are the biggest challenges and successes you’ve seen with keywording services?

Kevin:  I think the biggest challenge, certainly for us, has been dealing with the multitude of requirements and the different systems that our customers work with. It’s never really a thing where you are just sent some images and are allowed to do whatever you like to them and provide the best keywording or the best metadata you can.

Everybody has their own things that they want done. There are all these different standards, like you might be keywording for a Getty Images standard, or back when it used to be a thing, the Corbis standard, and so on and so forth.

Dealing with all of those different things I think is the real big challenge in keywording and delivering exactly what people want. That’s the real key.

I think the successes, kind of related, is that we’ve built systems that have enabled us to cope with all of those different things, things such as our own workflow system called Piksee, which it really did cut out an awful lot of handling time and wastage just dealing with sets of images.

Or we have our own client database which records and enables all our staff to know exactly, down to the contributor level, all of the things that you maybe want to do differently for one photographer over another when it comes to metadata or fixing your images.

Just a whole series of things that, when I first started, I didn’t realize all of these nuances would come into play, but they really are crucial to delivering a good service.

The result of that has been that our reputation is such that we tend to work for the big names ‑‑ certainly in the news, celebrity, and increasingly in the stock area as well ‑‑ like Associated Press, like Splash News, and like Magnum. It’s being successful in that we’ve managed to defeat the problem, I suppose.

Henrik:  As of early March 2016, how much of the keywording work is completed by people versus machines?

Kevin:  I guess it depends on how you work that figure out. In terms of, if the question is how many of the images that we work on are touched by human beings deciding on what keywords go into the images, that figure is really 100 percent.

But, and this is important, the technology that you have to assist them in doing that and doing a good job is quite considerable. I don’t think that’s it’s appreciated, I think, often by maybe photographers, or particularly amateurs out there, exactly what goes into what I’d call professional keywording as opposed to “seat of your pants” keywording.

We don’t sit there very often and keyword one image after another, searching into our memory banks, trying to come up with the best keywords. There are systems, vocabularies. There are ways for handling the images, organizing the images.

So much technology is involved there to really make the humans that we have the best that they can be.

I have to say, in that regard, what we always are doing ‑‑ and as I said earlier, we employ almost exclusively university graduates, people who have degrees in communication studies or English, or art history ‑‑ is that we’re trying to have the best supercomputer to do the keywording, which is the human brain, and the most educated and best-programmed supercomputer.

Then we add the technology on top. So, yes, 100 percent of the work in the end is done by people, but certainly with a lot of assistance from technology.

If you look into the future, the far future, I feel sure that one-day artificial intelligence will probably do a lot of things for all of us in all sorts of areas we’re not even vaguely aware of now.

We’re starting to see some of that happen already in all sorts of things to do with apps on your phones that can tell you how to do this, that, and that other, and account for your heartbeat; all sorts of things that are happening with artificial intelligence, which is great.

When it comes to keywording, what I see is not very flattering at the moment, which is not to say that it may not get there in the end. But I think what I need to do is try to put things in a little bit of perspective, at least from where I see it.

The level of complication that I was talking about earlier, which is really the key to good keywording, I think is where at the moment AI keywording falls down completely, and even before that it’s falling over some hurdles right now.

On my blog recently, I did a post about one AI provider, and they invite you to put test images in to see what they can do. Well, [laughs] the result was particularly unedifying, in that a lot of the keywords were just completely wrong. The point of the images was completely missed. They weren’t able to name