Tagging.tech presents an audio interview with Kevin Townsend about keywording services
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Keywording Now: Practical Advice on using Image Recognition and Keywording Services
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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 anybody in the images.
It was really a pretty poor effort, and even the examples they had on their website, showing what they considered to be successes, there were very few keywords in terms of what would be acceptable commercially.
Also, a lot of the keywords were extremely inane and almost pointless; certainly nothing that would fit into a vocab that you would be able to submit to Getty, for instance, or that would be acceptable to Alamy. This is a long, long, way from where it needs to get.
Perhaps the best analogy, that I could explain how I view things at the moment with AI and keywording, is a few years ago I went see the Honda robot which had come to town.
They had spent millions and millions and millions of dollars on this robot, and its big claim to fame was that it could walk upstairs, which it did. Not particularly well, but it did it. It was a great success, and everyone was very happy.
Thing is, any three‑year‑old kid in the audience could have run up and down those stairs and run around the robot many times.
I feel that AI keywording is a bit like that robot at the moment. Yes, it’s doing some rudimentary things, and that looks great, and people who think it’s a good idea and it’s all going to be wonderful, can shout about it, but it’s a long way from the reality of what humans are able to do. A long, long way.
I think where you have to consider the technology has to go is if you want to carry on the robot analogy, is to really be able to do the sort of keywording with concepts and meeting all these challenges of different standards, they have to be more like an android than they need to be like a robot that can assemble a motor vehicle.
Now, how long it’s going to take us to get to that sort of stage, I don’t know. I would be very doubtful that the amount of money and technology, and what have you, that would be needed to get us to that point is going to be directed towards keywording.
I’m sure there’ll be much more important things that sort of level of technology would be directed at. But certainly one day, maybe in my lifetime, maybe not, we’ll probably wake up and there’ll be androids doing keywording.
Henrik: Kevin, what advice would you like to share with people looking into keywording services?
Kevin: I think that it’s one of those things, it’s the oldest cliche, that you do get what you pay for, generally speaking.
We have had so many people who have come to us who have gone down the route of trying to save as much money as they could, and getting a really poor job done, finding it didn’t work for them, it wasn’t delivering what they wanted, and they’ve ended up coming and getting the job done properly.
For instance, at Magnum we have taken over the keywording there from what used to be crowd‑sourced keywording, which was particularly poor. That’s really made a big difference to them, and I know they’re very happy.
There are other examples that we’ve had over the years with people who’ve gone off and got poor keywording and regretted it. Just to use another old saying, no one ever regrets buying quality, and I think that is very true with keywording.
Henrik: Where can we find more information about keywording services?
Kevin: Right. We have a website www.keedup.com. We have a blog. We are also on Facebook, on Twitter, and on LinkedIn. We’re in a lots of different places. If you go there as a starting point, there are links there to other sites that we have. That’s a good place to start.
We have a site called coreceleb.com that’s a site which is an offshoot of what we do, which is focused really on editing down and curating the images that people are creating, so that you have more sales impact.
We also have brandkeywording.com, which is focused on adding information about brands that celebrities are wearing and using; not just fashion, but also what cars they drive, all sorts of things really to add new revenue streams, particularly for celebrity photo agencies, but also there’s no reason why that doesn’t include sports news and even stock.
Those are two which are really pretty important as well.
Henrik: Thanks, Kevin.
Kevin: Good. [laughs] I hope that will give people some food for thought.
Henrik: For more on this visit Tagging.tech.
Thanks again.
For a book about this, visit keywordingnow.com
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