TechCrunch asks and answers: Why image recognition is about to transform business. It’s a nice overview and of course we agree.
The key trends they identify include:
- Training material: open data. “Everyone from Google and Facebook to startups and universities use these open source picture sets to feed their machine learning beasts, but the big technology companies have the advantage of access to millions of user-labeled images from apps such as Google Photos and Facebook. Have you ever wondered why Google and Facebook let you upload so many pictures for free? It’s because those pictures are used to train their deep learning networks to become more accurate.
- Building blocks: Open-source software libraries and frameworks. “Once you have the data, it’s time to build a machine that can learn from it. Enter open-source software libraries. Freely available, these frameworks serve as starting points for building machine learning systems to service different kinds of computer vision functions, from facial and emotion recognition to medical screening and large obstacle (read: deer) detection in cars. These machine learning systems are then fed pictures from ImageNet and its ilk, proprietary images (aka Google Photos) or other sources (like anonymized, indexed clinical records).
- Ready-to-wear: Hosted APIs. “Not every company has the resources, or wants to invest in the resources, to build out a computer vision engineering team. Even if you’ve found the right team, it can be a lot of work to get it just right, which is where hosted API services come in. Carried out in the cloud, these solutions offer menus of out-of-the-box image recognition services that can be easily integrated with an existing app or used to build out a specific feature or an entire business.
- Custom computer vision technology. “Of course, it doesn’t have to be apples or oranges. Computer vision engineering teams don’t need to be Google-sized, and companies big and small that don’t want to build their own AI systems may still want robust, custom image recognition solutions. If a beauty or cosmetics company wants to find, say, pictures of people with high-volume hair to serve ads about body-minimizing shampoo, it’ll need someone to create a custom algorithm to search for high-volume hair, since that isn’t the first thing that the more commoditized solutions offer out of the box.
After reading these bullets you might feel the article was actual surveying why the technology is ready, but doesn’t talk about how it will transform business. Perhaps the market opportunities are so obvious?
We think there are some key trends driving the adoption of visual search for various niche markets. There are not necessarily small markets, but ones where visual search contributes something specific, such as mobile shopping, searching parts databases, and decision support for medicine. We write about these all the time.
Newer markets are opening up all the time, such as augmented reality – which is getting a lot of attention right now – and automated photo tagging. Eventually visual search will be like text search, a ubiquitous technology which can be applied to just about any application area. And of course we expect to be a part of it!