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.
Clemency Wright: Hi. I’m good, thanks Henrik. How are you?
Henrik: Good. Clemency, who are you and what do you do?
Clemency: I’m Clemency Wright. I’m the Owner and Director of Clemency Wright Consulting, which is a UK‑based business and we specialize in providing bespoke keywording services and metadata consultancy, primarily for the creative media industries.
We work with stock photo libraries. We also work with specialist image collections. We work with book publishers and a small number of online retailers. We do some collaborative work with software developers and technical consultants on various projects.
The purpose of our work, mainly, is to help our clients organize their digital assets. These could be visual or text‑based. The idea here is to make the assets found more quickly and more easily by their end users.
Initially, my role in this field was working within the stock photo library, in search data and search vocabulary for a major global stock photo library based in London.
From here, I’ve worked with specialist collections, where the nature of keywording is very different, and also in the museum and heritage sector; again, working with data in a very different format on a digitization process. The experience across those different fields is quite different when you look at it from a keywording perspective.
Just to clarify now, I’m a consultant for various businesses. This is really key, as the proliferation of visual media continues to grow. We’re very closely looking at the way we handle digital content, how we make sense of that digital content, how we make the information relevant, and more available to more people.
It has huge potential for our customers and for their end users, in terms of improving the search experience and the access to these assets. I think that pretty much summarizes where we are at the minute, in terms of who we work with, and what we provide for those people.
Henrik: What are the biggest challenges and successes you’ve seen with keywording services?
Clemency: One of the biggest challenges really is the perception that keywording is pretty much the same as tagging. Obviously with the rise of SEO, we’ve got some confusion here about what keywording is. We started keywording many years ago.
Obviously within librarianship and archival work, people were keywording as a way to retrieve information, which is still what we do, but I think the challenge here is breaking down these perceptions that it’s always a very basic way of tagging content.
We’re trying to differentiate between keywording which is, on its basic level, adding words that define an image or the content of an image, and high performance keywording which is very much a user‑focused exercise.
It’s a very 360‑degree look at the life cycle of the image and how that image will be ultimately consumed and licensed for use in the broader digital environment.
One of the challenges is highlighting the value of a high quality, high performance keywording project to the customers, and also their end users and the various stakeholders therein.
I think working with specialist collections can be quite challenging. We have to create bespoke keywording hierarchies and controlled vocabularies for these clients, which obviously makes the access to the content much more. The performance of that is much greater, but it can be challenging. It can be quite time‑consuming.
There’s a level of education that we need to have with our clients, to illustrate to them and demonstrate to them the return on investment that can be had from a good keywording methodology. By the methodology, I just wanted to define that, which links to the challenges that we have to do with technology and the extent to which we use controlled vocabulary systems and software, and the hierarchies that we build for our clients.
They help to define the depth to which we can classify content, and also, the breadth of that content. The content may be video footage, or it may be photography. It may be illustration.
Obviously, a challenge there is creating a vocabulary or a taxonomy that will cater for an ever‑increasing collection, one that is growing and evolving as businesses themselves incorporate new content into their collections.
Technology is a challenge, but it’s also a great facilitator in the work that we do. It allows us to embed a level of accuracy and consistency to the work that we do for our clients.
When you’ve got measures in place, and you’re creating controlled vocabularies and hierarchies, you’ve got systems there that make sure the right vocabulary is being applied, and it’s being applied consistently and accurately. There’s a level of support that the technology can offer, as well as it having its own challenges.
Perhaps on a more general level, keywording has been tarnished somewhat by some multi‑service agencies which are offering keywording as a bit of a sideline.
Perhaps their core business may be software or systems development or post‑production, but then, by offering keywording as an offshoot, some clients are going down that road and then discovering later on that actually, the keywording side of that was a bit of an afterthought. I think the methodologies and strategies in place have failed some of the clients that we work with, at any rate.
There’s a challenge there for us to make sure that we can differentiate between specialist keywording provider and an agency that offers keywording as an additional add‑on to their core business.
I think another challenge that is worth mentioning is the idea of offshoring keywording to agencies where perhaps the quality is compromised, and this is what I hear from clients. The feedback on some projects has been that there’s been a lack of understanding, due to language barriers mainly, but also cultural understanding of visual content.
It can be quite difficult, across the continents, for people to read and interpret visuals in the way that your market may perhaps be consuming those visuals. There’s a challenge in, again, educating people into the options and the various consequences of using these various agencies.
Henrik: Clemency, as of March, 2016, how much of the keywording work is completed by people versus machines?
Clemency: We know that there is a lot of work being done in auto‑tagging and systems that will automatically add keywords that are relevant to the content. In my business, we define automated systems and keywording in a much more specific way.
We use it to automate the addition of, say, synonyms, or to automatically translate keywords, or to automatically add hierarchical keywords, but I think, Henrik, what you’re asking really is about the image recognition technology, which is something we’re clearly aware of and we have been for some years now.
Image recognition is not something that we currently engage with or consult on. It’s in its infancy, and it will be very exciting to follow these developments, but for now it’s quite limited to reading data in a very simple form.
For example, color and shape, and to some extent, say, for example, number of people within an image is something image recognition technology can do, but I think there is quite a lot of documentation to support the idea that it’s very difficult for a machine to understand the sentiment behind an image, the concept, or the emotion.
I was thinking of a good example of an image of a person smiling. I’m not sure, I’m not convinced, the extent to which a machine could determine whether that smile is one of happiness or one of sarcasm, for example.
A person looking at an image will make a certain assumption about that smile. Maybe it is subjective, but I think it’s just something that’s perhaps a little bit too advanced for machines at the minute, to be able to read the emotional side of visual content, which is really the field that I’m most interested in, most active in.
I think the technology will improve, but underpinning that, it really depends on who will be responsible for managing the architecture and the taxonomy, and maintaining that, and editing it, and developing it, because of course, we need people to put the intelligence into the structure behind the technology.
Although we can increase efficiency, and that’s great, and we need to increase efficiency and reduce costs and increase productivity, I think there’ll be a lot of management required and people involved in making sure that the technology is delivering consistently relative results, and testing, and testing, and testing to see that this is how it’s happening.
But, as I say, we question the extent ultimately to which machines can interpret the more conceptual content and the visual content that we work with primarily, because visual media is always open to interpretation.
It’s a subjective form that perhaps machines will go so far, in terms of classifying basic content, which will be very, very helpful, and it certainly will help speed up the processes for people like us, but I think for the user we have to be mindful that relevance is really the most critical element of this whole process.
Henrik: What advice would you like to share with people looking into keywording services?
Clemency: I’ve been working with keywording for 14 years, and it’s a really varied and rich resource for anybody who’s interested in looking into keywording services.
I have a few ideas here, which are from my experience working with clients and from gathering feedback from clients, but I think the advice would be generally that there is no quick fix. Keywording isn’t something that you can pull out of a box. There’s no standard as such.
Even though we’re told there is a standard,the stock libraries that set standards are having to change those constantly because the distribution networks are changing and the media types are changing.
Be prepared for it to be a fluid project. If you start engaging with a keywording service. It will probably evolve over time. It will change over time, and that’s a good thing.
You need to be prepared to talk quite a lot about your business goals and objectives, perhaps more than you think. A good keywording agency will want to know a lot about your market, about your channels, your network, your distribution.
They won’t want just to see the content, because if they just see the content and they just add keywords, there’s a lack of connection from a marketing and a sales perspective. It’s very important for the keywording agency to understand your business and the context within which your business sits in the bigger picture.
Be prepared to be asked quite a lot of questions before you start engaging with a keywording provider.
The other main thing is to be wary, perhaps, of agencies that seem more focused on volumes and deadlines than they do quality. I alluded to that earlier on, with some of the options to offshore your work.
This can be a bit of a false economy. It can be, in the long run, more expensive to focus on volumes and timeframes. Quality’s always a good groundwork to base your keywording projects on.
Also, I’d advise people to work with someone who’s a communicator, someone who’s going to uncover the problem and really spend time and effort in solving that problem. They’ll want to see samples of your assets before they start giving you prices.
I think that’s a really important conversation to have. It’s really important to have good communication with your provider and also a good level of trust, so I’d advise you to find out who they’ve worked with and if possible try to speak to their clients, who they have worked with.
Another great idea would be to speak to picture researchers, because they use keywords day in and day out. They’re on stock photo websites, publishing, advertising, and design agencies.
People that use picture researchers and picture buyers would be a really great source of information, just to ask them what their experience is working with various providers of the content, because then from there you can track who has been investing well in good keywording, and what that means, and where the value is in that.
Most of the software that you look at will not do everything that you need it to do, and I think that’s another important thing to bear in mind from a technological standpoint, is systems are great and you’d do well to consult with someone who knows a lot about different systems.
But ultimately it’s best to configure a system that’s bespoke for your needs, so perhaps maybe investing a little bit more time than you first anticipated in researching systems that will be fit for your purpose and give your clients the best experience as a user.
Henrik: Great. Where can we find more information about keywording services?
Clemency: There’s various resources online. There are some really interesting blogs. We can put links in here for you for your readers, if they’re interested. One great independent resource, which I think is fantastic for all industry news in general, is Photo Archive News, which is a news aggregation. They list services and providers that you might want to contact and speak to.
You’ll also find information about keywording services on stock library websites. For example, Alamy has a list of resources*, and there are marketing services such as Bikinilists listing various resources available to the industry, but also mentioning keywording agencies that you might be able to work with across the globe. There are keywording agencies based in the US. There are agencies in New Zealand and across Europe.
I think, just to go back on the conversation previously, there’s a lot of research to be done. It does take a little bit of time, but I think when you find an agency that really understands what you’re looking for then you’ve got that conversation to have with them about what you’re specifically looking to achieve.
Henrik: Thanks, Clemency.
Clemency: Yes, thanks, Henrik. I hope it’s been a useful insight into the world of keywording.