Image retrieveal with the consumer in mind

As a continuation of my blog post Assumptions about the end user I want to explain what else should be thought of when designing image retrieval systems with the end user in mind.

Don’t cause the user more work

To summarize the post I mentioned above: “Algorithms should not create new work for the user, but remove (some of) it.” An algorithm should be rather conservative in its decisions, because a user will perceive an algorithm that, for instance creates wrong tags, that the user have to correct in the end, as faulty at not helpful at all.

Don’t dethrone the user

Also to often there is no option for the user to easily override the decision of the algorithm, without the need to disable it and losing all the support.

Lifelong learning

The algorithm should not only allow me to retag an image or move it to a different cluster, but use this information to retag other affected images and make better decisions in the future.

For instance Wang et al. show in Intelligent photo clustering with user interaction and distance metric learning how it is possible to use corrections made by the user to improve the distance calculation for photo clustering.

Solving the wrong problem

Unfortunately unconstrained* object recognition is still far from solved and useable. The best system so far is the one from Alex Krizhevsky (University of Toronto) using Deep Convolutional Neural Networks.

His system achieved a top-5 error rate** of 15.3%, compared to 26% of the second best system for one of the most demanding benchmark databases with 1.2 million images and 1000 object classes.

That’s very impressive, but it also means, that every 6th image gets assigned 5 labels, which are incorrect.

Nevertheless this system was so ground breaking that he together with his supervisor, Geoffrey Hinton, and another grad student where hired by Google in March of this year.
This system now runs the google+ photo search.

But do we need such a system? What does it help you if the algorithm detects that there is a plant or a chair in your images? Isn’t it much more useful to analyze the scene of the picture, to tag pictures with broader scene descriptions like, group picture, living room or mountains?

In 2010 a team from MIT and Brown University showed, that even with existing methods on can achieve 90% recognition for 15 different scene classes like office, living room, inside city and forest with only 100 training images per class.

The authors wanted to push their new dataset, that contains nearly 400 scene classes, for which they reach a recognition rate of just under 40%. While academically much more demanding and thus interesting, I don’t think consumers have a use for a system that can differentiate an oil refinery from an ordinary factory most of the time.

I am convinced that a simpler system, that gets a few categories right ‘all’ the time, is much more useful.

* unconstrained means that the algorithm does not need the environment or the object to be controlled in some way.
Most working system only work with lighting or background, perspective and with no or limited clutter and occlusion.

** top-5 error rate is the fraction of test images for which the correct label is not among the five labels considered most probable by the model

AI, the new secret weapon in the cloud photo-storage war.

Gigaom posted an article on “The Dropbox computer vision acquisition that slipped under the radar“. But I think it the article should have been called:

AI, the new secret weapon in the cloud photo-storage war.

Okay, this title is probably a hyperbole. But all the big internet companies offer a way to store and share your photos online. And to make their offer more compelling Yahoo, Google, and Dropbox all recently bought computer vision start-ups that will provide image recognition for their user’s uploaded photos. While Yahoo bought LookFlow, Google bought DNNresearch.

Microsoft is researching on image recognition for a long time and I am sure they will soon integrate some of their algorithms into their cloud products. And Facebook just founded an internal AI group.

And to get a look into the future without having to upload all your photographs to the internet, try the iOS app Impala. The app will analyse and categorise all your photographs on your device. It was created by EUVision technologies, a spin off of the University of Amsterdam commercializing their research efforts. 

After the negative conclusion from my last post about the closure of Everpix these are positive news for the machine learning market.

The end of Everpix, a sad week for photographers and machine learning researchers.

This week the photo storage service Everpix announced, that they will close down. They did not have enough paying costumers and could not find new investors.

That is sad. Not only because it was the world’s best photo startup according to the Verge, but also because it was the only company besides Google that used new machine learning techniques to help people manage their photo mess.

everpix home screen

Everpix home screen

Their closure can be seen as an indicator that end users and investors are not ready yet to spend additional money on machine learning algorithms.

Flashback mail

Flashback mail

Having read some articles and the associated comments[1, 2], it is clear to me that not their use of sophisticated machine learning algorithms but the daily ‘flashback’ email with pictures taken on the same day in previous years was the more popular feature. In fact, I did not even see one single comment about the algorithms that analysed the pictures.

But maybe their algorithms were just not good enough.

Unfortunately I could not try out their algorithms myself. My pictures just finished processing a few days before they announced to close down. But I found a comment of one of the founders on Hacker News, saying that they used a deep convolutional neural network with 3 layers for the semantic image analysis. This is the same technology Google now uses for their photo search.

But they were unhappy with the results of the algorithm so in January this year they changed their approach as their CTO, Kevin Quennesson, explains in ‘To Reclaim Your Photos, Kill the Algorithm’.  He writes: “If a user is a food enthusiast and takes a lot of food close-ups, are we going to tell him that this photo is not the photo of a dish because an algorithm only learned to model some other kind of dishes?” They found that the algorithm’s errors were not comprehensible for the end user.

So they planned to change their system. As I understand it, their old system learned and used concepts independent of the single user. But the new system also uses pictures of the same user to infer the content of a new picture. He calls this “feature based image backlinks”.

Explanation Feature-based Image Backlinks

The graph shows how a picture of a dish can be correctly identified because the content can be inferred by similar pictures of the user that the system identified correctly before. – from Quennesson’s blog post

Regardless of the success of Everpix, I think using the context of an image more is a helpful and necessary approach to build systems, that will reliably predict the content of an image in the future.

In any case I wish we would hear more about the underlying algorithms, what they tried, what worked and what not.