Many of you have asked us where the name “TinEye” originated. We’ll give you a quick hint: ROBOTS.

We at Idée love robots, machines, and all things mechanical. In fact, our office is filled with mechanical friends!

So a little history: back in 2000 when we were brainstorming cool names for our image recognition technology, it was no surprise that robots came to mind. Not just any robots, but those awesome tin toy robots of yore.

Yes, futuristic robots trump our human abilities in so many ways: their tin arms are stronger, their tin brains are faster, and their ‘tin eyes’ are keener…

Et voilà, the birth of TinEye! Well not really, as when we registered TinEye in the year 2000 – oh that has a nice ring to it – there was no TinEye, no reverse image search engine and no image recognition breakthroughs yet. There was a team, excited about changing image search.

We love our reverse image search engine name today but back then, had you been a participant in our brainstorming sessions you would have seen a polarized and totally divided team. Seriously who calls a search engine TinEye? Doesn’t the expression “tin ear” mean insensitive to music or subtleties in certain situations. If we called our reverse image search engine TinEye, wouldn’t we send the wrong message right out of the gate? And so did the conversations go… and go… and go… until we bit the bullet because we have a CTO who is obsessed with image search and when he becomes obsessed with a name, well it is game over for the rest of us!

TinEye’s “eyes” may be faster and keener than us mere mortals when it comes to finding image matches in billion image collections, but there are some things that human eyes can still do better! Like choosing the right colour palette to suit the mood of an image, or knowing just where to crop a photo to make it “pop”. TinEye on the other hand is laser focused on being the best reverse image search engine possible.

Fast forward to today: TinEye is the best reverse image search engine in the world (that’s what our fans tell us!) and TinEye has entered your search vocabulary in “hey, TinEye that image.”

For those of you wondering if we were inspired by Brandon Sanderson’s Mistborn trilogy: the story is awesome and the Tineyes in it are neat, but no, Mistborn was not even written when we picked our name. Bright minds think alike?

And there you have it, the etymology of our little robot TinEye!

Some shots from around the office…

[Photography by Melina]

Now if you want the play with the real deal in multicolour searching head over to the Idée lab!

Love how a blog post on LifeHacker about Google’s colour search moves into a full discussion of Idée’s technologies namely: TinEye and Multicolor search. Fun times in the Ideeplex!

I am obsessed these days with search, particularly searching user generated content. You can’t use what you can’t find. I know that everyone else is more concerned about trust and accuracy: like in can you trust what you are reading and is it accurate. Search will find the accuracies and inaccuracies all the same! Searching photographs is particularly painful since we typically rely on keywords to be associated with images to find them. It is a starting point but it is limiting. Large scale image searching on the web is still in its infancy. We have seen a lot of development in the search space this year, with the introductions of visual search features, colour searching, keywords + geotags but we still have a ways to go. What I have been thinking a lot about is the creation of a world visual repository: imagine (soon) being able to take a photograph of anything and getting back (useful) information about what you photographed, via mobile devices preferrably.

How far are we from this dream? I say: not very far. Not very far. Start work on stealth project now!

More than likely, Google is untouchable until some company — a start-up, I’ll bet — redefines what search is in a way that makes us feel like Google’s broad, unfiltered text search is old and cumbersome. The way we felt about DOS after the Mac appeared, or dot-matrix printers after ink-jet.

To keep up with this volume of information, our systems have come a
long way since the first set of web data Google processed to answer
queries. Back then, we did everything in batches: one workstation could
compute the PageRank graph on 26 million pages in a couple of hours,
and that set of pages would be used as Google’s index for a fixed
period of time. Today, Google downloads the web continuously,
collecting updated page information and re-processing the entire
web-link graph several times per day. This graph of one trillion URLs
is similar to a map made up of one trillion intersections. So multiple
times every day, we do the computational equivalent of fully exploring
every intersection of every road in the United States. Except it’d be a
map about 50,000 times as big as the U.S., with 50,000 times as many
roads and intersections.