Tagging.tech presents an audio interview with Georgi Kadrev about image recognition

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 Georgi Kadrev. Georgi, how are you?

Georgi Kadrev:  Hi, Henrik. All good. I am quite enthusiastic to participate in the podcast.

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

Georgi:  I’m Co‑Founder and CEO of Imagga, which is one of the pretty good platforms for image recognition as a service. We have auto‑tagging and auto‑categorization services that you can use for practical use cases.

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

Georgi:  In terms of challenges, I think, one of the biggest ones is that we, as human beings, as people, we are used to perceive a lot of our world through our eyes. Basically, when people think in general about image recognition, they have a very diverse and a very complete picture of what it should do.

Let’s say from optical character recognition or recognizing texts, to facial recognition of a particular person, to conceptual tagging, to categorization, all these different kinds of aspects of visual perception.

People typically have expectations that it’s the same technology or the same solution, but actually, quite a lot of different approaches needs to be engaged into the actual process of recognizing and understand the semantics of the image.

In terms of successes, like addressing this, I can say that not surprisingly the deep learning thing that is quite a big hype in the last few years have been a huge success into the more conceptual or class‑level object recognition. This is what it is as a type of object.

Is it a bottle? Is it a dog? Is it a cat? Is it a computer? Is it mobile phone? and so on. This has become pretty practical, and right now we can say that we are close to human level in recognition of a lot of different classes of objects.

At the same time, in some other spaces, like lower recognition, like facial recognition, we also see quite big appreciation rates that allow for practical applications.

I can say one of the good things is that we are more and more closely to automating, at least, part of the tasks that needs to be performed by a computer, replacing the need for manual annotation of photos for different use cases.

In terms of challenges, maybe I would also add that you still need a lot of data, a properly annotated data. In machine learning and in deep learning in general, it’s very data‑greedy, so we need an enormous amount of samples to really make something robust enough and practical enough.

We still see the gathering a high‑quality dataset is one of the challenges. This is something that we also try to internally address. It helps us be more competitive in terms of quality and the technology.

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

Georgi:  What we definitely see that there are more and more services. Some of them are pretty good quality that try to automate different aspects of image recognition that I briefly tackled.

We see even big players like Google starting to offer services for some of those things like what they call label recognition or what we call tagging, what they call optical character recognition or most of the vendors call it that way.

We also have seen logo and facial recognition being quite popular and being utilized more and more in different kinds of brand monitoring services.

At the same time, from the perspective of a bit of downside of visual recognition, something that we see when we talk about highly artistic images or some more specific art or other types of specific content, still the technologies needs to be customly‑trained for that.

If possible at all to train a classification‑based image recognition to recognize different kinds of artistic images or different kinds of very specialized image content.

It’s related with what I had mentioned in the beginning, that if you have a specific task, sometimes you need a specific approach. Deep learning to a certain extent has addressed this, but still it’s not like one-size-fits-all solution. We see that in a lot of cases the customers need to define a specific problem so that they can have a very good and precise specific solution.

Henrik:  As of March 2016, how much of image recognition is completed by humans versus machines?

Georgi:  I would say, [laughs] honestly depends on the task. We’ve seen some cases that machines can be better than humans and not just in theory, in practice.

For example, if we train a custom classifier with the human‑curated data set, and then we do some kind of testing or validation, we see that the errors, the things that are reported as errors in the learning process can actually mean errors by the people.

It’s mistaken when it has annotated the photo so that then it’s false reported as an error, although it’s correct. In a way, this is promising because it shows the automation and consistency that machines can do is pretty good in terms of precision.

At the same time, there are tasks where if you have a lot of explicit or implicit knowledge that you need to get in order to resolve an automation task. A lot of background knowledge that people have is not available for the machine and then you need to figure out a way how to either automate this or use a combination between a computer and a human, or you can decide this as a fully humanic task.

Still, it’s not approachable by tec