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

Assumptions about the end user

I am in the middle of a little literature review on using machine learning for photo organisation and came across a statement that struck me as misconceived. The paper’s topic is segmenting photo streams into events and states at the end of page 5:

We believe that for end users, having a low miss rate is more valuable than having a low false alarm rate.

I believe this is a false assumption that will lead to frustrated end users. Out of my own experience I am convinced that the opposite is true.

They continue: “To correct a false alarm is a one-step process of removing the incorrect segment boundary. But to correct a miss, the user must first realize that there is a miss, then figure out the position of the segment boundary.”

Similar to face detection users will be happy about a correct detection but unhappy about an algorithm that creates wrong boundaries they have to manually correct.

And if we assume, that a conservative algorithm still finds all the strong boundaries, the user might not miss the not detected boundaries after all.

Algorithms should not create new work for the user, but remove (some of) it.

Content based image classification with the bag of visual words model in Python

Even with ever growing interest in deep learning I still find myself using the bag of visual word approach, if only to have a familiar baseline to test my new fancy algorithms against. I especially like the BoW demo script from the VLFeat team, that reaches a solid 65% accuracy on the, admittedly outdated, Caltech101 dataset. The script has the advantage that it is contains all the usual steps in one script (feature extraction, training of the classifier and evaluation of the whole pipeline) and that it can also be easily adapted to other datasets.

The only problem was, that it is a Matlab script and Matlab licences are in my experience often scarce due to their high price even for research institutes. So I rewrote the script in Python using the uncomplete VLFeat Python wrapper.

You can find my code as usual on github: https://github.com/shackenberg/phow_caltech101.py

In case you are just diving into the world of BoW I recommend my minimal BoW image classifier code, which might be easier to understand.

International Computer Vision Summer School (ICVSS) 2012, a review

This blog post is intended to be informative for students who plan to attend ICVSS for the first time and to give feedback to the organizers, because I had not time to fill out my feedback-form back there 😉

It is based on my experiences of participating in this years ICVSS 2012. In this post I will only talk about the organizational side. Of course the lectures are the most important part of the summer school, but it is easier to establish if the lectures are relevant to you than to find out if you would enjoy the summer school.

TLDR: I liked it very much. If you are a 2nd or 3rd year PhD student and have 1300€ to spend, definitely go!

What is ICVSS?

The International Computer Vision Summer School is a yearly one-week conference for students in the field of computer vision held in Sicily, Italy. In contrast to normal conferences it is less formal and the aim is to learn rather than to present. For that reason they invited renowned researchers and professors to talk about he “theoretical and practical aspects of real Computer Vision problems as well as examples of their successful commercialisation”.

The summer school’s program comprises of

  • lectures,
  • workshops (which are a like practical lectures),
  • poster presentations by the attendees,
  • a reading group,
  • a essay contest (Brady Price),
  • a written exam and
  • social events.

It is organized by the University of Catania, Sicily, Italy and the University of Cambridge, UK.

The Program

All five days followed roughly the same pattern. They started with breakfast at 8h followed by two lectures before lunch. After lunch there was one more lecture followed by a coffee break. The after-coffee-break program varied throughout the week:

  • The first two days students were presenting their posters,
  • on the third day there was a guided tour to ancient city of Ragusa Ibla,
  • on the fourth day the afternoon was filled with the Reading Group and
  • on the last day Friday, there was the examinations, the student presentations and an award ceremony.

Generally speaking everything was very well organized, even – if you allow me this cliché – from a German point of view. The issues I was told or experienced myself were mostly due to circumstances outside of the ICVSS staff’s influence like airlines or the hotel being sloppy.

In fact all lectures and events were starting so punctual that I had trouble being always punctual 😉

They also managed to give the summer school a nice pace. There was no downtime to get bored and you never had to stress to see or do the things you wanted.

Lectures

My only issue with the program was that the lectures were too long. Some lasted 2h without break. They changed that halfway through the summer school and I hope that sticks for the next years. I would suggest cutting the all lectures into blocks of 50min followed by 10 min breaks.

Poster Sessions

In the poster sessions students could present their work to the other students to get feedback, which is always very valuable as the other participants are from a similar field with out being too familiar to you work to ask the right questions.

The was also a competition for best poster. The two winners received a money price (700€) and were asked to present their work in a short talk on Friday after the examinations. (Congratulations again to Christof Hoppe with Photogrammetric Camera Network Design for Micro Aerial Vehicles)

My only quarrels where that some people did not go to the second floor, because there were not enough signs. Furthermore the rooms were too crowded, especially in the corners where posters were hanging on each side, although there was unused room upstairs in the gallery.

Reading Group and Essay Contest

The aim of the reading group is to teach and practice the skill of reading research papers. To take part you have to prepare a homework “studying (not just reading) one or more topics provided by the school committee, and tracing the ideas as far back as you can.” This year the topic was image features and last year it was shapes. The groups and individuals with the best and most interesting submissions will be asked to present their work during the reading group at the summer school followed by a discussion. The group or the individual with the best presentation is awarded a money price of 1000$. If I remember correctly only 18 groups or individuals participated and you can listen and take part in the discussion even if you haven’t sent in homework. Never the less, I would urge you to hand in something as the organizer of the reading group, Stefano Soatto, give extensive feedback.

The essay contest (also called Brady Price) was about discussing the current and the future “real world” social impact of computer vision technology. There were two topics to choose from (Urban Landscapes and Computer Vision and Medicine) and the two winner were asked to read out their essay and received 600€ in price money.

Exam

The exam consisted of 37 multiple choice questions covering the lectures and workshops. You had to answer 17 correctly to pass the exam and receive a separate certificate. Only a few of the lecturers gave useful example questions after their lectures so we were not really sure what to expect. In the end the questions were quite fair and sensible. I would say, you can pass studying in your room at night if you paid attention in all the lectures.

Social Events

I actually attended my first beach party during the summer school. The other activities were also very enjoyable. Look forward to them.

Venue, Accommodation and Food.

When I told my friends about the summer school I found myself using the following phrase a lot:

It was the nicest prison I have ever stayed in.

Maybe I am just not used to resort vacation, but I think this description fits. The hotel is remote and you wont be able to leave the place and find anything in walking distance except for a small fishing village and a beach.

Nevertheless I liked the place, as it provides everything one needs. I even was able to buy some swimming pants and flip-flops I forgot back in Germany.

For each meal the hotel would provide a varied buffet and I can’t remember anyone complaining about the food. If you are vegetarian it is definitely doable without starving taste-buds.

Internet

The internet connection was bad! Very bad!! In theory there was WiFi in the lobby of the hotel, the lecture hall and in the foyer of the lecture hall, where the organizers have their temporary office. (Note: No WiFi in the rooms!)

But in practice the WiFi connections were so slow that sometimes not even emails would load and there was a connection timeout of like 5 mins after which one had to re-enter a personal code of nearly 20 characters, which made using the internet on the phone way more annoying than fun. As I wrote the resort is very remote, so it is probably cost prohibitive to get faster internet for the few internet addicts visiting once a year. Which is a pity as the organizers try to push the use of social media (facebook/twitter) during the conference, which is imho a fun idea. But my proposal would be to shut off the WiFi in the lecture hall. As a result, people are less tempted (and less frustrated) by the internet during the talks and there would be more bandwidth left for the people sitting in the foyer (doing important stuff ™)

Money

If you coming from Europe this summer school will cost you roughly 1650€. (600€ for the school, 750€ for a single room and 300€ for the flight.  You can cut the costs to 1250€ by reserving a bed in a 4 person room (450€) and by booking your flight early with websites like skyscanner.com.  This is still quite expensive, but I was quite satisfied in the end. There were no hidden costs, they didn’t seem to throw out money for totally unnecessary things and they didn’t try to sell things, which I cannot stress enough.

More warning than recommendation is this résumé by Roman Shapovalov, who choose a hotel in the near village to stay for ICVSS 2010.

People

As the organizers told us in the opening presentation, the most important part of this conference are the people we meet. For some working on their own at their home universities this might be a first time to feel as part of a community. And the process of becoming a community is deliberately amplified by the choice of such a remote venue. For every meal and for every activity we stayed together, so you got to know the other people very fast.

Also most of the lecturers stay for more than a day, so this was a great chance to interact with them in a very relaxed environment. Some even brought their families, which shows how much they enjoy this summer school themselves.

Summary

I liked it a lot and I think I will go again. If not next year, in 2014, even though I will have to pay it from my student scholarship. I’ll be probably booking a bed in a four-person room, which makes it cheaper and more interactive.

My recommendation is to go as soon as you finished your literature review and have a some results to present. Make sure you have something to show and talk about. Than you can learn and profit from the connections you make and the tips you receive a long time.

There is also the CVML Summer Schools organized by INRIA, France, which ended this year just before ICVSS. If you have enough money then go to both, otherwise choose with regard to the speakers.

Tipps

  • Prepare an elevator speech
  • Leave your notebook at home. Remember there is no internet and you are there to meet people. You can bring yours slides on a USB stick if you are planning on winning the competition.
  • Don’t go to bed too early, sleep after lunch. You don’t miss anything and it is too hot to do anything anyway.
  • Plan one or two days of extra stay in Sicily. It is easier to ignore the beach next to the hotel, when you know you have time afterwards to go to the beach – you pay for the return flight anyway! My recommendations would be Siracusa and Stromboli.
  • Don’t forget your swimming pants! You need them and they are expensive in the resort.

Further reading:

Did you attend ICVSS? What was your experience?
Thinking about going and having questions left?
Leave a comment!

[update] corrected breakfast time and modified intro

A list of lists of PhD resources

On your way to become a PhD, you not only have to learn how to do research, you also have to learn how to communicate your ideas comprehensible in text and speech, how to build the tools you need and how to survive in the microcosmos of supervisors, colleagues and under grad students of your research lab. But you are not the first to go through all this and people have written extensive advice for every problem you might encounter.  And as they are so popular right now, I present you here my: 

List of lists of PhD resources for computer scientists.

List of lists of lists

List of lists of lists!?

The most condensed summary I have found on the website of my work group IUPR. It is a good starter and gives one an overview of all the things one has to keep in mind and pay attention to.

From the most condensed to the most comprehensive. This collection links to nearly 100 articles on Ph.D. dissertation/research, presentations, writing,  reviewing/refereeing,  being a faculty member, job hunting, learning English and more. The list is overwhelming.

Links to documents on giving talks and writing papers and proposals.

from the UCSD VLSI CAD LABORATORY

If you did not actually study computer science (like me) or your courses mainly covered logic and reducing NP-complete problems, this site can probably help you a lot. Software carpentry is about learning the skills to write reliable software and using the existing tools efficiently. The website offers tutorials on basic programming, version control, testing, using the shell, relational databases,  matrix programming, program designing, spreadsheets, data management, and software life-cycles.

[UPDATE] How could I forget:

Like the well known Stackoverflow.com Academia is “a collaboratively edited question and answer site for academics and those enrolled in higher education.” It is still in its beta phase, but growing everyday. I like the aspect, that it will be always more current and extensive than all the pages only maintained by individuals or single work groups. And if you can’t find the information you need, you can always ask for help.

So what do you think? Do you find these resources useful? Some of them are already quite old. Do you think they are obsolete? What are you tips? Which collections did I forget?

Can Stack Exchange save scientific peer review? [Update]

One of the few things everybody seems to agree on is that the scientific review process, especially for computer science, is broken. I wont go into details here as there are many sources on the net.

But personally I found Yann LeCun’s pamphlets for “A New Publishing Model in Computer Science” inspiring. He proposes an open, karma-based online repository which I will summarize as follows:

  • In this system authors post their papers as soon as they feel, that there finished. The publication is put under version control and is immediately citable.
  • “Reviewing Entities” (RE), individuals or groups like editorial boards, then choose papers they want to review or accept review requests from authors.
  • REs do not “own” papers exclusively, so RE can choose to review any paper at any time. Papers can be reviewed by multiple REs.
  • The reviews are published with the paper and are themselves citable documents like regular publications.
  • Reviews are furthermore rated by readers. Good reviews will generate “karma” points for the RE, to show the usefulness of their review.
  • Additionally RE’s “karma” will increase if they are the first to positively review a paper which is than later rated as high quality by other REs as well. As a result RE will have an incentive to be the first to review good papers.

I will not repeat LeCun’s explanations on how it works in detail and why it would be superior to the existing system. Instead I want to point out how very similar this approach is to the Stack Exchange (SE) QA websites. Stack Exchange is a network of over 70 Q&A websites, with stackoverflow.com, a Q&A site on programming, being the first and largest one. On Stack Exchange websites everyone can ask questions which can be answered by all the members of the community. Both questions and answers will be rated by the community, so users are incentivized to write useful answers to questions which are relevant to many other users in order to gain reputation.

Especially if you have used a SE website, it is hard to ignore the similarities. Even though the SE framework was build to solve a different problem, I can see it being adapted to act as a repository for LeCun’s publishing model. Publications would be questions and reviews would be answers. I can only make out following necessary changes.

  • There needs to support for groups (RE),
  • high level users should not be permitted to change other people’s posts anymore and
  • the ‘answer’ functionality has to be removed.

Everyone who follows the two founders of Stack Exchange, Jeff Atwood and Joel Spolsky, knows, how determine both are to remove all diversion of their vision for Stack Exchange, so it wouldn’t be possible to be officially part of the SE community. But there is also OSQA, the open source copy of SE. Using this service makes it seem possible to implement the necessary features.

So, what do you think? Can Stack Exchange save scientific peer review?

[UPDATE]

LeCun was so generous to comment on my article via e-mail. He confirmed that his views on the peer review process and his model haven’t changed and agrees that creating the technical infrastructure shouldn’t be too hard. He already received several offers from possible volunteers, but the project is still missing a highly competent developer(-team) to “own” the project.

Disclaimer: I am not the first one to bring Stack Exchange on the table, but I found the other approach far less concrete.

Paper: Rendering Synthetic Objects into Legacy Photographs

Inserting 3D objects into existing photographs

 

This fascinating video presents a new method to insert 3D objects into existing photographs. It is based on the research of Kevin Karsch, Varsha Hedau, David Forsyth and Derek Hoiem  (all University of Illinois at Urbana-Champaign). Their main contribution is the algorithm, which generates the light model for the scene. The algorithm needs only one photograph and a few manual markings by a novice user together with a ground truth data set to create a near real life insertion. The ground truth data set was generated with 200 images from 20 indoor scenes under varying lighting conditions.

The video is well done and I am surprised whats possible, but I like to see how much user input is really necessary and how well the algorithm and the ground truth perform with other images. What do you think?

More details can be found at Kevin Karsch’s website.