Idée’s Multicolr Search Lab

Thursday 27th November, 2008

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.

MultiColr

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.

MultiColr engine

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.

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The State of Image Search

Wednesday 6th August, 2008

There’s currently a lack of direction in the image search products offered by the leaders in the field. Each offering is quite different, and none have fully realised revenue streams. This is a quick summary of the current state of play.

Text search by any other name

Some image search engines learn about images solely by leveraging image meta-data and nearby text in parent documents. It’s a little like identifying a photograph by the name on the album cover and the writing on the back of the photo. This was an ideal solution for text search engines like Google and Yahoo, who could leverage their existing data and infrastructure.

Getting smarter

Microsoft’s Live Search have recently started broadening the mainstream by adding the capability to analyse the images themselves. For example, the Live Search team have added the ability for their system to recognise faces.

Playing the name game

The big players in search get revenue from serving up relevant advertising, but so far none of them have successfully monetised image search. Currently image search serves as a loss-leader that exists to support their search brands, a visible sign that they’ve still got chips in the big game.

That doesn’t mean they’re sitting on their hands. Both Microsoft and Google employ researchers in the area of image comparison and classification so expect big developments from them in 2009.

Pure image search start-ups

There are a few start-ups with an eye on the prize of being the first to monetise image search. Being smaller and more maneuverable than the big players they’ve got off the ground faster, but have yet to build up significant numbers. Start-ups to keep an eye on include Picitup (find similar images, celebrity face comparison), Riya/Like (text-driven image search and product search), and the Toronto-based Idée Inc (copyright monitoring, colour-based search).

These guys are hungry for revenue, so I expect to have fresh news in Q4 this year.

The home team

I work for Pixsta, another image search start-up. We’ve pulled together the basis of a decent team, and should start taking over the world shortly. As for what we’re working on, I’ll write more when I know what’s safe to write about outside NDA :o)