Henrik de Gyor: This is Tagging.tech. I’m Henrik de Gyor. Today, I’m speaking with Emily Klovitz.
Emily, how are you?
Emily Klovitz: I’m doing great. How are you, Henrik?
Henrik: Good. Emily, who are you and what do you do?
Emily: I’m a DAM consultant, marketer, and digital asset manager for Bynder. We’re an award-winning digital asset management software that allows brands to create, find, and use content such as documents, graphics, and videos. Before joining Bynder, I worked as a digital asset manager for JC Penny. I have MLIS, my masters in library information studies from the University of Oklahoma. I’ve worked with hundreds of different clients on their DAM implementations, providing best practice and consultation. Because I work with clients, I’m often able to see the very real world implications of what AI tagging can actually be like with live collections of content. The successes and challenges are very real, very tangible, and that’s not always something that you see when you’re watching a webinar or a product demo.
Henrik: Emily, what are the biggest challenges and successes you’ve seen with image recognition?
Emily: For challenges, of course, there are some challenges and opportunities for improvement when it comes to AI tagging. I think many of them have to do with the application and configuration of the AI, not necessarily the technology itself. Today, once specific limitation currently in our own implementation of AI, we only have US American English tags at this time, so we wanted to make a claim on the AI space very quickly, so English to start with was part of our MVP for AI features. Obviously, there’s more to come in the future. I think some other limitations include things like only certain file types are scanned, such as JPEG and IMG, so there’s an opportunity to extend this out to things like video, documents, etc. Many other companies are already doing this, companies like Ancestry.com for example or even DocumentCloud, which scans your documents through Thomas Reuters Open Calais to extract entities, topic codes, events, relations, social tags. In addition, there’s a full list of AWS limitations on the recognition site as well, which is what we use. But in terms of what more general things I think need to be considered challenges are things like mistakenly tagging something in a way that’s hurtful or harming in some manner. Those are things that don’t usually become apparent until after the fact. I think that AI tagging is very much in its infancy in terms of its application and that we’ll see it greatly grow and mature in the coming years where we may start to see challenges like information and privacy concerns pertaining to facial recognition. Being able to opt out of these things will basically be a big need for clients.
As far as successes go, AI tagging detects objects, scenes, and can identify thousands of objects such as vehicles, pets, furniture, and it provides the confidence for, which simply tells you how confident the AI is that that tag is relevant and accurate. It’ll detect scenes within an image, so things like a sunset or a beach. This has really big implications for search filtering and curating very large image libraries. From my perspective alone, the time-saving factor for DAM managers, digital asset librarians, content managers, and admins of the system is probably one of the biggest successes for AI tagging. They spend an enormous amount of time and resources on metadata application alone. It’s tedious thankless work, but absolutely necessary so that people can find the assets they need.
In terms of other things, I think it’s also helping to put a minimum viable metadata on a very large digital asset collection that may otherwise remain untagged. For DAM, it means that uploaded images get auto-tagged, helping with categorization, identification, and searchability of assets that could possibly otherwise be buried in the depths of your collection without metadata.
Henrik: Emily, as of July 2017, how do you see image recognition changing?
Emily: Becoming a defacto feature of digital asset management systems and less of a fun/nice to have feature, like more of a novelty feature, it’s becoming something you have to have.
Henrik: What advice would you like to share with people looking into image recognition?
Emily: This is a good one. If you can, provide a sample of your assets to different vendors and ask for results. It’s very easy to see a webinar or a product video showing 100% accuracy and it’s really neat, but it’s also really important to try out a wide variety of image assets to see where the real limitations are for each image type and the associated algorithms.
Henrik: Where can we find more information?
Emily: There’s lots of places on the internet you can find more information about AI tagging. You can find information from us specifically on our blog, blog.bynder.com. Amazon’s recognition website has a great FAQ that you can check out. We also did a presentation at the photo metadata conference in Germany, the IPTC Metadata Conference on image recognition and AI. There’s a PDF and a video available of this presentation on IPTC.org.
Henrik de Gyor: This is Tagging.tech. I’m Henrik de Gyor. Today I’m speaking with Martin Wilson. Martin, how are you?
Martin Wilson: I’m very well, thank you. How are you?
Henrik: Good. Martin, who are you and what do you do?
Martin: I am a director at Asset Bank. Being a director, I’ve done an awful lot of different things over the years. I have done some development on our product, Asset Bank. I’ve done sales and I’ve done consultancy while rolling out the product.
Just to explain a little bit about what Asset Bank is as a product, it is a digital asset management solution. Digital asset management is often shortened to DAM. A DAM solution helps clients and the users to organize the digital assets that almost every organization owns and makes use of nowadays.
By digital asset, we mean primary files. Things like images, videos, documents and all of those. A digital asset has an awful lot of value to an organization and it’s very important that they can find them easily, that they don’t waste money recreating digital assets that they already have, and that the assets themselves are used properly in a way that’s consistent with the brand of the organization.
Henrik: Martin, what are the biggest challenges and successes you’ve seen with image and video recognition?
Martin: Let me first start by saying how I think that image recognition has a potential to have a really big impact on my industry, digital asset management. Digital asset management is all about being able to find images and then use them properly. That’s the purpose of the DAM system. There’s an old adage which people use and it says that a DAM system is only as good as the metadata that is associated with the assets. The reason for that is, a million images, if you have a million images in any system it’s almost impossible to find the image you want without some sort of a search and or a browse function. Those searches and browse functions at the moment rely on what we call metadata that it is associated with the assets. That metadata is things like title or caption of an image, description, perhaps some keywords that been put in, maybe some information about how that can be used, the image can be used.
The result of this is that people, humans, spend an awful lot of time entering the metadata that is associated with digital assets. Usually, within an organization, the processes, the workflows that are associated with using a DAM application involve uploading one or more or many digital assets, typically images or videos, and then manually entering the data by, for example, looking at the image, seeing what it’s about, what the subject is, who’s in it maybe if it’s of people and then just actually typing in that data.
As you can imagine, that takes a lot of time. It’s also considered quite boring by most people. For that reason, it’s often skipped or not done really well. If it’s not done really well, the data associated with the assets is incomplete and therefore it’s very hard for it to turn up in the right searches.
The idea that it could be automated, this process, and have a computer work out what’s in the image and tag the digital assets appropriately is enormous. It’s almost like the Holy Grail of the upload process for DAM systems.
There was an awful lot of excitement when, for example Google Cloud Vision came out with their service. It’s what called an API which enables other applications to make use of the image recognition functionality. There’s a lot of other services as well that have come out in the last couple of years like Clarify, is another one.
When they came out, lots of DAM vendors got very excited and rushed to add the functionality into their own applications. We did the same. About a year ago we started a project with the objective of developing a component that could be used with Asset Bank in order to add auto-tagging capabilities to asset bank.
Let me just describe some of the challenges then that we found in doing that and when we rolled out some of our clients, the challenges they found. One of the challenges, I suppose which is always like a umbrella challenge over all of it, is people’s expectations.
Humans are very good at looking at images and working out what’s in it. They’ve also got a lot of domain knowledge. Usually, they understand, for example, their products. They can look at a product shot and say, “Yeah, that’s product F-567”, or whatever the code is. It’s actually very hard for computers to do that well. That problem hasn’t been solved that well yet.
What we found is, when compared with how humans tag images, the results coming from the auto-tagging software or APIs was not, to be frank, not of good enough quality for most cases. That’s the second specific challenge then, really. The quality of the raw results coming back from the software. The image, the visual recognition software was not quite good enough for use in most organizations, especially in a commercial sense.
That’s not say that it’s not useful. I’ll come on to that in a bit. What we found, on to the successes, what we found was that certain clients who had more generic or general images, the results were much better. We’ve got some clients who are tourists boards. They’ve got images of landscapes and scenery. Most of the image recognition software is quite good at finding the subjects and suggesting keywords for those types of images.
One of the reasons for that is that most of them have been trained on image data sets, that are images that are found on the internet for example. Of course they’re going to be generic. The other end of the spectrum, where we found it didn’t work that well was for clients that have got quite bespoke business domains or subject domains, images of their own product range. Very hard for these fairly generic image recognition software APIs to be able to come up with the right keywords for those sorts of image.
That’s possibly where there are still gaps. That might be something we’ll talk about in a minute about the future, which is the inability for a lot of this tagging software to learn from bespoke data sets.
Henrik: Martin, as of December 2016, how do you see image and video recognition changing?
Martin: I think it’s fair to say that it’s in it’s infancy at the moment. It’s only since it’s become available through the online cloud services or web services that people have found it very easy to start using this technology in their own applications. It’s only been the last couple of years, that really has kind of taken off as something that can be openly or easily used.
Now I think the vendors of this sort of software are learning very quickly from real use cases. I think it’s quite an exciting for where the commercial or non-commercial application of this software can go. I think if we first focus a little bit more on the current problems, that gives some insight into where the software might go, what direction it might go in.
I was just talking then about one of the problems being that is very generic at the moment, the tags that you get back from the online services are going to be fairly generic. That’s obviously the case if you understand how they work and how they learn. I think very quickly we’re going to see these services, and I know some are already, offering the ability for you to train them with your own data sets. That then opens up the application a lot more widely.
One of the things that image recognition and artificial intelligence, in general, is the context in which they’re operating. It’s much easier for image recognition software to work well if it is working within quite a narrow context. As an example, if you’re talking about, or if you want to try and get the software to recognize your product range, then if it’s trained on images that are of product range, and therefore the context is only products within your product range, then it’s a lot easier for it to recognize the right products, rather than having to think of every product that it’s ever seen an image of in the entire world.
Just to reiterate that I think the ability to train the software in bespoke data sets and for it to concentrate on in effect, domain-specific subjects, I think that’s a must and that will start to happen.
I think we will see quite a few hybrid solutions. What we found when we were doing our investigation into the software and what we ended up doing within Asset Bank or within the components which we call QuickTagger that works with Asset Banks is, coming up with hybrid human and computer interaction model where the tags that were being suggested by the visual recognition software were not just accepted as that’s job done. They were used to then group the images so a user could very quickly change the tags that weren’t right.
They could, for example, accept some of the tags, because they’re the right tags and the human agreed with the computer in effect, but then they could quite easily change the tags that were wrong. The key thing here is that the grouping was still being done pretty successfully. Although the tags that the image recognition software was suggesting might not be right, it was recognizing that certain images were of the same subject. That therefore meant that a human could go in and say, “Okay, I’ve got 50 images here that are all of a particular, I don’t know, model of car. They’ve all been grouped together, so that makes it really easy for me as a human to now type in the right name of the car or the model of the car.”
I think that idea, that where we are right now with this technology is that can help facilitate, speed up the human interfaces. That’s a quite a powerful idea I think, but where … I think that will continue, so we’ll see an evolution of that. I think we’re quite a long way off just being able to say, “Okay, you get on with it, computer. Tag these up.” I think we’re going to see improvements and sort of evolution of the idea of humans and computers working together in this auto-tagging sphere.
Henrik: Martin, what advice would you like to share with people looking at image and video recognition?
Martin: The first thing I would say is about expectations management. If you are used to having tags generated by humans who know what they’re doing, they understand the domain, the subject domain of the images that they’re tagging, you are likely to be fairly disappointed I would say in the results for most cases.
That’s one thing. See beyond the raw results you’re getting back from the tagging software. Look to how you might use the tags though to your advantage. For example, in hybrid solutions.
Consider what subject matter you’ve got, what your images are actually of and tailor your expectations accordingly. If you’ve got a lot of images that are of fairly generic subjects, you might find a lot of value from the auto tags. If you’ve got quite specific subjects, be prepared to potentially be a bit disappointed and or to have to put in quite a lot of work to either start training some of the software that you’re using or looking at how you can sort of augment the results with human interactions.
Sorry, another bit of advice is shop around. Have a look at the different services that are available. They’re fairly different. We built our QuickTagger in such a way that we can plug in the different services that are available, so we could just simply change it to work with Google Cloud Vision or with Clarify and there’s ten other potential candidates that I could list off the top of my head and probably more out there. They give different results. Some of them are better for different applications as well and different subjects. Usually, very simple to get a free trial and try out the software that’s there. That would be my last bit of advice. Shop around with the auto-tagging technologies that are available.
Henrik: Martin, where can we find out more information?
Martin: More information on our product Asset Bank is available on our website, which is www.assetbank.co.uk. If you’re interested in particular in how we, in the experiments that we’ve done and the components that we’ve got for Asset Bank, QuickTagger, then just fill in our contact form and express that interest. I would personally be very happy to talk to people about what we found.
There’s some information about QuickTagger that we’ve developed on our website as well. If you’re interested in finding about the different technologies that are available out there for you to use within your own application, there’s a lot. Personally, I would recommend now the cloud-based ones, because it’s much easier to get up and running with those. There’s quite a lot of information, meaning if you just typed in ‘image recognition software’ or ‘image recognition APIs’, you’ll see there’s quite a few good articles that people have put together on Quora and so on that have done the research for you. Use that as a starting point because as I say, things change all the time. New APIs come out. Do your research, but there is a lot of information available on the internet about this.