To my surprise, Google Goggles actually launched last night, not 12 hours after I posted about it yesterday. I’ve just spent a while playing around with it on my Android handset. Search times are, as expected, much more than one second, more in the anticipated 5-10 second range. Good to see that even Google can’t break the laws of physics. The app shows a pretty-but-pointless image analysis animation to make the wait seem shorter, almost exactly like my tongue-in-cheek suggestion from yesterday.
The engine covers all the easy verticals (books, DVDs, logos, landmarks, products, text, etc). The recognition quality is very good, though the landing pages are often a bit useless. It will take a bit of living with it to see how much use it is as a tool rather than a tech demo.
The major worry is that it may end up being too broad-but-shallow. For example, they do wine recognition, but the landing pages are generic. Perhaps visual wine recognition would be better built into Snoot or some other dedicated iPhone wine app. Or Google could take the route Bing recently took with recipes, and build rich landing pages for each vertical. Because of the nature of current visual search technology, Goggles is essentially a number of different vertical searches glued together, so this is more feasible than it would be for web search.
The tubes are a-buzz with some news that Google are working on a mobile visual search system. No surprise there really, but it is interesting to see the current state of their prototype. The info comes from a CNBC documentary called Inside Google. A clip is up on YouTube, and the relevant section starts three minutes in.
The main thing that surprised me is that the interviewer mentioned response times of less than a second. I find that somewhat unlikely, and I think it’s probably a case of a journalist misunderstanding some fine (but crucial) distinctions.
In terms of the actual visual search engine, there’s no problem. Our own visual search engine can serve searches in a couple of hundred milliseconds, even for millions of images. The issue is transmitting the user image to the server over the mobile phone network. Cell networks are still pretty slow, and have high latency even to transmit 1 byte. Even after aggressive compression of the image and after playing some other fancy tricks, we see typical user waiting times between 4 and 5 seconds over a typical 3G connection. On a 2G connection, the system is basically unusably slow . I strongly suspect the Google app will perform similarly. In many cases it’s still much faster than pecking away on an on-screen keyboard, though because the user is waiting, it can feel longer. It would almost be worth giving the user a pointless maths puzzle to solve, tell them it’s powering the recognition, and they would probably be happier!
In any case, the Google demo is an interesting, if not unexpected, development in the visual search space. While we’re waiting to see the final version of Google Visual Search, Android users can try out our PlinkArt app, which does visual search for famous paintings. It’s live right now!
I’ve let this blog go very quiet while I was working on finishing my thesis (done now!). However, today my brother got a helicopter pilot’s license, so I though I would mark the occasion by posting some videos showing how his fancy skill might soon be redundant :). Here are some cool results from Nick Roy’s group at MIT:
It’s a pretty cool system. Robots that do the full autonomous shebang, from SLAM to path planning to obstacle avoidance, are still quite rare. To do it all on a helicopter is just showing off.
I’m off to RSS 2009 in Seattle next week to present a new paper on FAB-MAP, our appearance-based navigation system. For the last year I’ve been hard at work on pushing the scale of the system. Our initial approach from 2007 could handle trajectories about 1km long. This year, we’re presenting a new system that we demonstrate doing real-time place recognition over a 1,000km trajectory. In terms of accuracy, the 1,000km result seems to be on the edge of what we can do, however at around the 100km scale performance is really rather good. Some video results below.
One of the hardest things to get right was simply gathering the 1,000km dataset. The physical world is unforgiving! Everything breaks. I’ll have a few posts about the trials of building the data collection system over the next few days.
So the visual search story of the day is that Amazon has acquired SnapTell. This is a really natural fit – SnapTell have solid technology, and Amazon are one of the best use cases. Not too surprised to hear the deal has been done – SnapTell has been conspicuously quiet for several months, and word was that they either had to exit or secure another funding round before the end of the year. So congratulations are in order to everyone at SnapTell on securing what seems like an ideal exit.
The big question now is how this changes the playing field for other companies in the visual search space. I would assume Amazon will move SnapTell’s focus away from their enhanced print advertising service and concentrate on image recognition for books, CDs, DVD, etc. (Up to now, Amazon has been doing this with human-powered image recognition, which was nuts.) While this makes perfect sense for Amazon, it’s going to mean more rather than less opportunities for companies still focused on the general visual search market.
So I guess this is an ideal point to mention the open secret that I’m currently co-founding Plink, a new visual search engine similar in capability to SnapTell. While our demo shows some familiar use cases, we’re working on taking the technology in some entirely new directions. Visual search is very young, there’s a whole lot still to do! Anyone interested in visual search, feel free to contact me.
Congratulations to everyone at Willow Garage for reaching Milestone 2 in the development of the PR2 robot. 26.2 miles of autonomous indoor navigation, including opening eight doors and plugging in to nine power sockets. We’ve been watching the video in the lab with serious robot envy. Very cool!
1. What is fragile should break early while it is still small. Nothing should ever become too big to fail. Evolution in economic life helps those with the maximum amount of hidden risks — and hence the most fragile — become the biggest.
Then we will see an economic life closer to our biological environment: smaller companies, richer ecology, no leverage.
A sensible plan, but unfortunately Mr. Taleb’s faith in biology is misplaced.
Why the Dinosaurs got so Large 19th-century palaeontologist Edward Drinker Cope noticed that animal lineages tend to get bigger over evolutionary time, starting out small and leaving ever bigger descendants. This process came to be known as Cope’s rule.
Getting bigger has evolutionary advantages, explains David Hone, an
expert on Cope’s rule at the Institute of Vertebrate Paleontology and
Paleoanthropology in Beijing, China. “You are harder to predate and it
is easier for you to fight off competitors for food or for mates.” But
eventually it catches up with you. “We also know that big animals are
generally more vulnerable to extinction,” he says. Larger animals eat
more and breed more slowly than smaller ones, so their problems are
greater when times are tough and food is scarce. “Many of the very
large mammals, such as Paraceratherium, had a short tenure in the
fossil record, while smaller species often tend to be more
persistent,” says mammal palaeobiologist Christine Janis of Brown
University in Providence, Rhode Island. So on one hand natural
selection encourages animals to grow larger, but on the other it
eventually punishes them for doing so. This equilibrium between
opposing forces has prevented most land animals from exceeding about 10 tonnes.
Dinosaurs had skewed incentives and took on too much tail risk! If even evolution falls into this trap, God help the bank regulators…
Today’s edition of the New Scientist news feed includes an article about my PhD research. How nice! They called the article ‘Chaos filter stops robots getting lost’. This is kind of a bizarre title – ‘chaos filter’ seems to be a term of their own invention :). Still, they mostly got things mostly right. I guess that’s journalism!
Whatever about the strange terminology, it’s great to see the research getting out there. It’s also nice to see the feedback from Robert Sim, who made a rather impressive vision-only robotic system with full autonomy a few years ago, still quite a rare accomplishment.
For anyone interested in the details of the system, have a look at my publications page. New Scientist’s description more or less resembles how our system works, but many of the specifics are a little wide of the mark. In particular, we’re not doing hierarchical clustering of visual words as the article describes – instead we learn a Bayesian network that captures the visual word co-occurrence statistics. This achieves a similar effect in that we implicitly learn about objects in the world, but with none of the hard decisions and awkward parameter tuning involved in clustering.
I was at a lunch talk today by Nick Bostrom, of Oxford’s Future of Humanity Institute. The institute has an unusual mandate to consider the really big picture: human extinction risks, truly disruptive technologies such as cognitive enhancement, life extension and brain emulation, and other issues too large for most people to take seriously. It was a pleasure to hear someone thinking clearly and precisely, in the manner of a good philosopher, about topics that are usually the preserve of crackpots. Prof Bostrom’s website is a treasure trove of papers. An atypical but perhaps robot-relevant example is the Whole Brain Emulation Roadmap.