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Audio, Image and video keywording. By people and machines.

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Tagging.tech interview with Kevin Townsend

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.

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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 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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Tagging.tech interview with Brad Folkens

Tagging.tech presents an audio interview with Brad Folkens about image recognition

 

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

Keywording_Now.jpg

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 Brad Folkens. Brad, how are you?

Brad Folkens:  Good. How are you doing today?

Henrik:  Great. Brad, who are you and what do you do?

Brad:  My name’s Brad Folkens. I’m the CTO and co‑founder of CamFind Inc. We make an app that allows you to take a picture of anything and find out what it is, and an image recognition platform that powers everything and you can use as an API.

Henrik:  Brad, what are the biggest challenges and successes you’ve seen with image recognition?

Brad:  I think the biggest challenge with image recognition that we have today is truly understanding images. It’s something that computers have really been struggling with for decades in fact.

We saw that with voice before this. Voice was always kind of the promised frontier of the next computer‑human interface. It took many decades until we could actually reach a level of voice understanding. We saw that for the first time with Siri, with Cortana.

Now we’re kind of seeing the same sort of transition with image recognition as well. Image recognition is this technology that we’ve had promised to us for a long time. But it hasn’t quite crossed that threshold into true usefulness. Now we’re starting to see the emergence of true image understanding. I think that’s really where it changes from image recognition being a big challenge to starting to become a success when computers can finally understand the images that we’re sending them.

Henrik:  Brad, as of March 2016, how much of image recognition is done by humans versus machines?

Brad:  That’s a good question. Even in-house, quite a bit of it actually is done by machine now. When we first started out, we had a lot of human-assisted I would say image recognition. More and more of it now is done by computers. Essentially 100 percent of our image recognition is done by computers now, but we do have some human assistance as well. It really kind of depends on the case.

Internally, what we’re going for is what we call six-star answer. If you imagine a five-star answer is something where you take a picture of say a cat or a dog. We know generally what kind of breed it is. A six-star answer is the kind of answer where you take a picture of the same cat, and we know exactly what kind of breed it is. If you take a picture of a spider, we know exactly what kind of species that spider is every time. That’s what we’re going for.

Unsupervised computer learning is something that is definitely exciting, but I think we’re about 20 to 30 years beyond when we’re going to actually see unsupervised computer vision, unsupervised deep learning neural networks as something that actually finally achieves the promise that we expect from it. Until then, supervised deep learning neural networks is something that are going to be around for a long time.

What we’re really excited about is that we’ve really found a way to make that work in a way that’s a cloud site that customers are actually happy. The users of CamFind are happy with the kind of results that they’re getting out of it.

Henrik:  As of March 2016, how do you see image recognition changing?

Brad:  We talk a little bit about image understanding. I think where this is really going is to video next. Now that we’ve got some technology out there that understands images, really the next phase of this is moving into video. How can we truly automate and machine the understanding of video? I think that’s really the next big wave of what we’re going to see evolve in terms of image recognition.

Henrik:  What advice would you like to share with people looking into image recognition?

Brad:  I think what we need to focus on specifically is this new state of the art technology. It’s not quite new but of deep learning neural networks. Really we’ve played around…As computer scientists, we’ve screwed around a lot, for decades, with a lot of different machine learning types.

What really is fascinating about deep learning is it mimics the human brain. It really mimics how we as humans learn about the world around us. I think that we need to really inspire different ways of playing around with and modeling these neural networks, training them, on larger and larger amounts of real-world data. This is what we’ve really experimented is in training these neural networks on real-world data.

What we’ve found is that this is what truly brought about the paradigm shift that we were looking to achieve with deep learning neural networks. It’s really all about how we train them. For a long time, when we’ve been experimenting with image recognition, computer vision, these sorts of things. If you look at an applesto apples analogy, we’re trying to train computers very similarly to if we were to shut off all of our senses.

We have all these different senses. We have sight. We have sound. We have smell. We have our emotions. We learn about the world around us through all of these senses combined. That’s what form these very strong relationships in our memory that really teach us about things.

When you hold a ball in your hand, you see it in three dimensions because you’ve got stereoscopic vision, but you also feel the texture of it. You feel the weight of it. You feel the size. Maybe you smell the rubber or you have an emotional connection to playing with a ball as a child. All of these senses combined create your experience of what you know as a ball plus language and everything else.

Computers on the other hand, we feed them lots of two-dimensional images. It’s like if you were to close one of your eyes and look at the ball, but without any other senses at all, not a sense of touch, no sense of smell, no sense of sound, no emotional connection, none of those extra senses. It’s almost like if you’re flashing your eye for 30 milliseconds to that ball, tons of different pictures of the ball, and expecting to learn about it.

Of course, this isn’t how we learn about the world around. We learn about the world around through all these different senses and experiences and everything else. This is what we would like to inspire other computer scientists and those that are working with image recognition to really take this into account. Because this is really where we’ve seen as a company the biggest paradigm shift in image understanding and image cognition. We really want to try to push the envelope as far the state of the art as a whole. This is kind of where we see it going.

Henrik:  Where can we find more information about image recognition?

Brad:  It’s actually a great question. This is such a buzzword these days, especially in the past couple of years. Really, it sounds almost cheesy but just typing in a search into Google about image recognition brings up so much now.

If you’re a programmer, there’s a lot of different frameworks that you can get started with image recognition. You can get started with one of them’s called OpenCV. This is a little bit more of a toolbox for image recognition. It requires a little bit of an understanding of programming and a little bit of understanding of the math and the sciences behind it. This gives you a lot of tools for basic image recognition.

Then to play around with some of these other things I was talking about, deep learning, neural networks, there’s a couple of different frameworks out there. There’s actually this really cool JavaScript website where you can play around with a neural network in real time and see how it learns. This was really a fantastic resource that I like to send people to, kind of help them, give them an introduction to how neural networks work.

It’s pretty cool. You play with it, parameters. It comes up with…It paints a picture of a cat. It’s all in JavaScript, too, so it’s pretty simple and everything.

There’s two frameworks that we particularly like to play around with. One of them is called Cafe, and the other one is called Torch. Both of these are publicly available, open source projects and frameworks for deep learning neural networks. They’re a great place to play around with and learn, see how these things work.

Those are really what people tend to ask about image recognition and deep learning neural networks, that’s the sort of thing. I like to point them to because it’s great introduction and playground to get your feet wet and dirty with this type of technology.

Henrik:  Thanks, Brad.

Brad:  Absolutely. Thanks again.

Henrik:  For more on this, visit Tagging.tech.

Thanks again.

 

For a book about this, visit keywordingnow.com


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Tagging.tech interview with Maura Framrose

Tagging.tech presents an audio interview with Maura Framrose about keywording services

 

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

Keywording_Now.jpg

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 Maura Framrose.

Maura, how are you?

Maura Framrose: Hello, Henrik.

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

Maura: My name is Maura. I am an independent keywording specialist. I worked with Getty for five years in the early 2000s, integrating partner data to standardize inputting on search methodology.

I keyword for photographers on their own sites and for distribution and provide consultancy services for archives and migration. I assess existing data and requirements to streamline, simplify, reduce database noise, and to ensure search results are consistent and relevant.

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

Maura: The demand for high volume at low cost can lead to compromise in the quality of keywords and to keyworders being exploited. A good edit is important before images even reach keywording services, as is an understanding of the importance of investing time and attention into keywords.

There is a lovely challenge in the link between what is being tagged and what people actually want to see. As this shifts and changes, keeping up with search trends and adapting keywords to reflect and fulfill expectations beyond the basics, while still being relevant, is a lovely challenge to meet.

Success is a clean and clearly relevant search return on any given keyword or its variants, as this tends to improve sales figures and return visits to websites.

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

Maura: We have to keep up with technology as it changes. We drive those changes ourselves with bugs and fixes, and wish lists improvements, and enhancement features. In tagging an image well, you reflect its true value.

Good software with hierarchy and synonym functionality improves speeds and can automate relevant keywords onto images. These hierarchies, in themselves, require human input and maintenance as language changes and new content is added.

Thanks to the Internet, we are able to research and double check facts much more easily than we could 15 years ago. A curiosity and willingness to check facts is one of the elements which encourages good keywords on an image.

While there is image recognition software in development, which to some extent may be able to automate keywords to images, as a keyworder you’re looking for the attributes which make the image distinct and of human interest.

You’re able to evaluate concepts, emotions, relationships. Who drew the map, who sailed with it, on which voyage, and when? This curiosity for significance may only be answered by machines where the intelligence exists and has been accurately programmed and input to data files in the first place.

Henrik: Maura, what advice would you like to share with people looking into keywording services?

Maura: I would advise you to not be looking for the cheapest option. Having it done cheaply is not necessarily having it done well.

Henrik: Maura, where can we find more information about keywording services?

Maura: We wrote an article with the British Journal of Photography, and I also have several papers planned. I’m on LinkedIn and work with the keywording guild called Word Association.

Henrik: Thanks, Maura. For more on this, visit Tagging.tech.

Thanks again.

 

For a book about this, visit keywordingnow.com