This morning I had another play with the Multicolr Search Lab from visual search outfit Idée Inc and decided to make some notes, which I’ve posted below.
Idée in context
Idée are one of the bigger players in the visual search space, although they currently occupy quite a different market to my new team Pixsta.
Idée’s biggest product so far is TinEye, which is used to find uses of a single specific source image across the Internet. The main commercial use appears to be detection of copyright infringment, a service they provide to photographers and copyright owners for a fee. To my (admitedly limited) knowledge they’re the only company offering this specific service.
The Multicolr Search Lab (MSL) is a proof-of-concept that demonstrates Idée’s ability to index image by colour. As an image-based ‘labs’ project, the UI naturally reflects a mixture and Google Labs functionality and Flickr’s Web 2.0 styling. Its clean and simple. I like it a lot.
Naturally as a ‘labs’ project it has no direct revenue stream, but it’s a nice demo and there may well be use for this type of technology in some areas: Interior design for example; add a simple hook-up to a printing service like Photobox and you’ve got a revenue stream for photographers and your own service.
As Multicolr is currently running at an adequate speed over a stated 10 million images I’d guess that the underlying technology is ready for at the least enterprise scale applications, if not internet scale.
From my previous play with Multicolr I has certain expectations as to how it worked internally.
In any search application you need precision, but you also need to be able to bring back close matches if no direct matches exist. Since MultiColr is using RGB hex values as query terms I’d suspected that they’d rounded those hex values to match their chosen quantisation, and were matching on those approximate colours.
This isn’t what they’re doing, as I discovered when I changed the colour specified in their nicely RESTful URL to a subtely different colour (one that should fall within the same quantisation bin). If they were using naive quantisation then the results would have remained the same. In fact they changed.
So although they may still be using some form of quantisation to avoid gaps in queries, it seems like they’re also allowing the raw colour value (either in RGB or a different colour space) into the index, and scoring images appropriately based on each query, i.e. the quantisations seen in the UI may well be totally arbitrary.
All in all, an good piece of work. Well done Idée.