Milabra, a new image recognition company offering other websites a B2B solution, has launched to the public. In conjunction with the launch, the company has also announced a $1.4 million funding round with a number of private investors contributing. Milabra’s engine is capable of identifying a broad range of images, “from puppies to porn” (their words), and can be applied in a variety of ways like adult content prevention and automatically tagging images with searchable metadata.
The company’s image recognition engine is supposed to mimic some natural tendencies seen in the human brain (Milabra’s team includes PhD neuroscientists from Columbia University). Rather than trying to identify photos based on their similarity to a template image (for example, using a portrait of the Mona Lisa to find identical matches or slightly modified versions of the famous painting), Milabra can “learn” to recognize certain scenes or objects without having to refer to a source image. So when searching through a library of images for dogs, Milabra doesn’t need to constantly compare each image with its database of known ‘dog’ images – instead, it can look for traits that it has learned to associate with “doggyness”, leading to an engine that is more flexible and speedier.
While Milabra offers a demo version of its software on its website, its primarily focus is on creating solutions for other businesses (you can view a list of partners the company has already signed here). Clients are able to manage their photos through comprehensive CMS backend, which can be customized on a per-customer basis.
The company reports an average of 20ms needed to identify each image, which it says is substantially lower than most of its competition. It also reports a success rate of 98% when identifying faces, and its pornography filter (which could be used by family-friendly sites to screen user-generated content) is also nearly 99% successful. However, the software has its limits: in order to add a new filter (say, a search that could identify all photos with baseball caps), customers will have to wait a few days for Milabra to implement their requests – there’s apparently no way to teach the application using your own photos.
During the demo I was shown, the company’s engine worked as advertised. A search for beach images successfully identified all photos taken at the beach, regardless of the lightening or camera orientation. Of course, this was using the company’s own database so it’s tough to tell how accurate the engine is in real-world scenarios. Milabra is by no means the first startup to tackle image recognition – there have been stream of similar solutions that have tried and failed, leading some companies to give up on automation and crowd source the identification to bored humans. So while Milabra may seem to work well, the real test will lie in how many major partners the company is able to sign (Milabra says some major deals are on the way, though they wouldn’t go into details).