Showing posts with label movie. Show all posts
Showing posts with label movie. Show all posts

All the summer schools

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This summer I attended two conferences (3DIMPVT, EMMCVPR) and two summer schools. I know my latency is somewhat annoying, but it's better to review them now then never. :) This post is about the summer schools, and the following is going to be about the conferences.


PhD Summer School in Cambridge

Both schools were organized by Microsoft Research. The first one, PhD Summer School was in Cambridge, UK. The lectures covered some general issues for computer science PhD students (like using cloud computing for research and career perspectives) as well as some recent technical results by Microsoft Research. From the computer vision side, there were several talks:
  • Antonio Criminisi described their InnerEye system for retrieval of similar body part scans, which is useful for diagnosis based on similar cases' medical history. He also featured the basics of Random Forests as an advertisement to his ICCV 2011 tutorial. The new thing was using peculiar weak classifiers (like 2nd order separation surfaces). Antonio argued they perform much better then trees in some cases.
  • Andrew Fitzgibbon gave a brilliant lecture about pose estimation for Kinect (MSR Cambridge is really proud of that algorithm [Shotton, 2011], this is the topic for another post).
  • Olga Barinova talked about the modern methods of image analysis and her work for the past 2 years (graphical models for non-maxima suppression for object detection and urban scene parsing).
The other great talks were about .NET Gadgeteer, the system for modelling and even deployment of electronic gadgets (yes, hardware!), and F#, Microsoft's alternative to Scala, the language that combines object-oriented paradigm with functional. Sir Tony Hoare also gave a lecture, so I had a chance to ask him how he ended up in Moscow State University in the 60s. It turns out he studied statistics, and Andrey Kolmogorov was one of the leaders of the field that time, so that internship was a great opportunity for him. He said he had liked the time in Moscow. :) There were also magnificent lectures by Simon Peyton-Jones about giving talks and writing papers. Those advices are the must for everyone who does research, you can find the slides here. Slides for some of the lectures are available from the school page.

The school talks did not take all the time. Every night was occupied by some social event (go-karting, punting etc.) as well as unofficial after-parties in Cambridge pubs. Definitely it is the most fun school/conference I've attended so far. Karting was especially great, with the quality track, pit-stops, stats and prizes, so special thanks to Microsoft for including it to the program!


Microsoft Computer Vision School in Moscow

This year, Microsoft Research summer school in Russia was devoted to computer vision and organized in cooperation with our lab. The school started before its official opening with a homework assignment we authored (I was one of four student volunteers). The task was to develop an image classification method capable to distinguish two indoor and two outdoor classes. The results were rated according to the performance on the hidden test set. Artem Konev won the challenge with 95.5% accuracy and was awarded a prize consisted of an xBox and Kinect. Two years ago we used those data for the projects on Introduction to Computer Vision course, where nobody reached even 90%. It reflects not just the lore of participants, but also the progress of computer vision: all the top methods used PHOW descriptors and linear SVM with approximate decomposed χ2 kernel [Vedaldi and Zisserman, 2010], which were unavailable that time!

In fact, Andrew Zisserman was one of the speakers. Andrew is the most cited computer vision researcher and the only person whose Zisserman number is zero. :) His course was on Visual Search and Recognition, including instance-level and category-level recognition. The ideas that were relatively new:
  • when computing visual words, sometimes it is fruitful to use soft assignments to clusters, or more advanced methods like Locality-constrained linear coding [Wang et al., 2010];
  • for instance-level recognition it is possible to use query expansion to overcome occlusions [Chum et al., 2007]: the idea is to use the best matched images from the base as new queries;
  • object detection is traditionally done with sliding window, the problems here are: various aspect ratio, partial occlusions, multiple responses and background clutter for substantially non-convex objects;
  • for object detection use bootstrapped sequential classification: on the next stage take the false negative detections from the previous stage as negative examples and retrain the classifier;
  • multiple kernel learning [Gehler and Nowozin, 2009] is a hot tool that is used to find the ideal linear combination of SVM kernels: combining different features is fruitful, but learning the combination is not much better than just averaging (Lampert: “Never use MKL without comparison to simple baselines!”);
  • movies are common datasets, since there are a lot of repeated objects/people/environments, and the privacy issues are easy to overcome. The movies like Groundhog Day and Run Lola Run are especially good since they contain repeated episodes. You can try to find the clocks on Video Google Demo.
Zisserman talked about PASCAL challenge a lot. During a break he mentioned that he annotated some images himself since “it is fun”. One problem with the challenge is we don't know if the progress over years really reflects the increased quality of methods, or is just because of growth of the training set (though, it is easy to check).

Andrew Fitzgibbon gave two more great lectures, one about Kinect (with slightly different motivation than in Cambridge) and another about continuous optimization. He talked a lot about reconciling theory and practice:
  • the life-cycle of a research project is: 1) chase the high-hanging fruit (theoretically-sound model), 2) try to make stuff really work, 3) look for the things that confuse/annoy you and fix them;
  • for Kinect pose estimation, the good top-down method based on tracking did not work, so they ended up classifying body parts discriminatively, temporal smoothing is used on the late stage;
  • “don't be obsessed with theoretical guarantees: they are either weak or trivial”;
  • on the simplest optimization method: “How many people have invented [coordinate] alternation at some point of their life?”. Indeed, the method is guaranteed to converge, but the problems arise when the valleys are not axis-aligned;
  • gradient descent is not a panacea: in some cases it does small steps too, conjugate gradient method is better (it uses 1st order derivatives only);
  • when possible, use second derivatives to determine step size, but estimating them is hard in general;
  • one almost never needs to take the matrix inverse; in MATLAB, to solve the system Hd = −g, use backslash: d = −H\g;
  • the Friday evening method is to try MATLAB fminsearch (implementing the derivative-free Nelder-Mead method).
Dr. Fitzgibbon asked the audience what the first rule of machine learning is. I hardly helped over replying “Never talk about machine learning”, but he expected the different answer: “Always try the nearest neighbour first!”

Christoph Lampert gave lectures on kernel methods, and structured learning, and kernel methods for structured learning. Some notes on the kernel methods talk:
  • (obvious) don't rely on the error on a train set, and (less obvious) don't even report about it in your papers;
  • for SVM kernels, in order to be legitimate, a kernel should be an inner product; it is often hard to prove it directly, but there are workarounds: a kernel can be drawn from a conditionally positive-definite matrix; sum, product and exponent of a kernel(s) is a kernel too etc. (thus, important for multiple-kernel learning, linear combination of kernels is a kernel);
  • since training (and running) non-linear SVMs is computationally hard, explicit feature maps are popular now: try to decompose the kernel back to conventional dot product of modified features; typically the features should be transformed to infinite sums, so take first few terms [Vedaldi and Zisserman, 2010];
  • if the kernel can be expressed as a sum over vector components (e.g. χ2 kernel $\sum_d x_d x'_d / (x_d + x'_d)$), it is easy to decompose; radial basis function (RBF) kernel ($\exp (\|x-x'\|^2 / 2\sigma^2)$) is the exponent of a sum, so it is hardly decomposable (more strict conditions are in the paper);
  • when using RBF kernel, you have another parameter σ to tune; the rule of thumb is to take σ² equal to the median distance between training vectors (thus, cross-validation becomes one-dimensional).
Christoph also told a motivating story why one should always use cross-validation (so just forget the previous point :). Sebastian Nowozin was working on his [ICCV 2007] paper on action classification. He used the method by Dollár et al. [2005] as a baseline. The paper reported 80.6% accuracy on the KTH dataset. He outperformed the method by a couple of per cents and then decided to reproduce Dollár's results. Imagine his wonder when simple cross-validation (with same features and kernels) yielded 85.2%! So, Sebastian had to improve his method to beat the baseline.

I feel I should stop writing about the talks now since the post grows enormously long. Another Lampert's lecture and Carsten Rother's course on CRFs were close to my topic, so they deserve separate posts (I already reviewed basics of structured learning and max-product optimization in this blog). Andreas Müller blogged about the recent Ivan Laptev's action recognition talk on CVML, which was pretty similar to ours. The slides are available for all MSCVS talks, and videos will be shared in September.

There were also several practical sessions, but I personally consider them not that useful, because one hardly ever can feel the essence of a method in 1.5 hours changing the code according to some verbose instruction. It is more of an art to design such tutorials, and no one can really master it. :) Even if the task is well-designed, one may not succeed performing it due to technical reasons: during Carsten Rother's tutorial, Tanya and me spent half an hour to spot the bug caused by confusing input and index variable names (MATLAB is still dynamically typed). Ondrej Chum once mentioned how his tutorial was doomed since half of the students did not know how to work with sparse matrices. So, practical sessions are hard.

There was also a poster session, but I cannot remember a lot of bright works, unfortunately. Nataliya Shapovalova who won the best poster award, presented quite interesting work on action recognition, which I liked as well (and it is not the last name bias! :) My congratulations to Natasha!

The planned social events were not so exhaustive as in Cambridge, but self-organization worked out. The most prominent example was our overnight walk around Moscow, in which a substantial part of school participants took part. It included catching last subway train, drinking whiskey and gin, a game of guessing hallucinating names of each other, and moving a car from the tram rail to let the tram go in the morning. :) I also met some of OpenCV developers from Nizhny Novgorod there.


MSCVS is a one-time event, unfortunately. There are at least three annual computer vision summer schools in Europe: ICVSS (the most mature one, I attended it last year), CVML (held in France by INRIA) and VSSS (includes sport sessions besides the lectures, held in Zürich). If you are a PhD student in vision (especially in the beginning of your program), it is worth attending one of them each year to keep up with current trends in the vision community, especially if you don't go to the major conferences. The sets of topics (and even speakers!) have usually large intersection, so pick one of them. ICVSS has arguably the most competitive participant selection, but the application deadline and acceptance notification are in March, so one can apply to the other schools if rejected.

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Art + Multimedia

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In December we visited a lecture about interaction between arts and sciences, namely between (primarily) visual arts and (again, primarily) multimedia studies (Russian page). The lecture was given by Asya Ablogina, who turned out to be a nice girl. Although she lacked any technical background, she was doing well. Surprisingly, there is a lot of artwork that exploits technical support in a witty manner, which I was unaware of, so the lousy stuff exhibited in the Moscow Museum of Modern Art is not everything one can do in this field!

The most interesting part was Asya's exposition of some masterpieces. Most of them were presented during 2009 Science as Suspense exhibit in Moscow (Russian). Nicolas Reeves is a famous Canadian architect, also known for his work on modelling biological systems. He used the output of biologically-inspired computer algorithms (such as genetic algorithms) to draw pictures. In Moscow he presented a project called Marching Floating Cubes: massive but light cubes float in the air. Their movements are controlled by tiny fans, although any little gust of wind can affect the movement. Each cube is equipped with an on-board computer, which helps to avoid collisions. The implemented algorithms are simple but stochastic, hence the behaviour is unpredictable. They are said to move like animal creatures. Here is the video:



A similar project was developed by Paul Granjon. He also tries to gift robots with animal behaviour. In the video below, robots are sexed, i.e. they are able to locate robots of the opposite sex and eventually end up in coitus. Another Paul's robot (the Smartbot, photo) creeps over the restricted space and always grumbles (just like Marvin!). I also enjoy the way Paul speaks:


The real idol in the sci-art community is Stelarc (see also his homepage, although it takes balls to get through the welcome page :). His talent is recognized by both scientists and artists (it is enough to mention he's an Honorary Professor of Arts and Robotics at Carnegie Mellon). He's best known for his experiments on his own body. For example, during a performance he allowed to control his body remotely over the Internet by muscle stimulation. Probably his favourite project is Prosthetic Head, which is in fact a 3D model of his own head. The head is learned from Stelarc's behaviour and speech, so it is able to communicate with people using colloquial information as well as non-verbal cues. It is interesting to try it in practice, but here is just a non-interactive demo video:



Stelarc is probably the only person on Earth who has three ears. In 2007 surgeons implanted an ear into his arm! It is not functioning now (just a piece of skin), but he plans to install an audio receiver into the ear and broadcast everything he hears over the Internet. I definitely recommend to look through his projects, there are decent ones.

Asya also presented her own works. She focus on different photographic techniques such as overexposure. Here are photos from her project Canvas of the Road compiled in a video. There is a play of words in Russian: the single word for canvas and road surface. On these photos car traces look like painter's strokes:


Another Asya's project is a short film series called Habitat. She showed only one episode with the title Habitat: MSU Main Building. The film was about a girl who is a PhD student in Moscow State University and described her place: a standard 8 sq.m. dorm room. The narrative was full of expressive epithets and metaphors about that awful room: the girl felt like in a cage, she also didn't like shared bathroom, etc. It's funny, because I live in a similar PhD student single room, and I'm quite happy with it, since I've been living 5 years in a shared room before. :)

The second lecturer, Vladimir Vishnyakov, presented his project called The Museum of Revived Photography. He used the photos of the XIX century to create image-based animation. The idea is technically simple: first segment people from the background, then use inpainting techniques to restore the background behind them, and animate the movements, but Vladimir referred to a programmer he works with as a real genius. In fact, all the stuff I post here varies from easy to moderately complex (except of Stelarc's projects), so you can do something similar too. The main problem is to come up with the concept. Come on then! ;)

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Papers, citations, co-authorship, and genealogy

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This summer I accidentally found out that there are two papers citing our CMRT 2009 paper. I was excited a bit about that since they were the first actual citations of me, so I even read those papers.

The first of them [Димашова, 2010] was published in Russian by OpenCV developers from Nizhny Novgorod. They implemented cascade-based face detection algorithm that used either Local Binary Patterns (LBP) or Haar features. The algorithm was released within OpenCV 2.0. They cite our paper as an example of using Random Forest on the stages of the cascade. However, they implemented the classical variation with the cascade over AdaBoost.

Another citation [Sikiric et al., 2010] is more relevant since it came from the road mapping community. They address the problem of recovering a road appearance mosaic from the orthogonal views to the surface. They contrast their approach with ours in the way that theirs do not employ human interaction. In fact, we need human input for recognizing road defects and lane marking, rectification is done in the previous stage, which is fully automatic.

The rest of the post is devoted to some interesting metrics and structures concerning citations, co-authorship and supervising students.

Citations

The number of citations is a weak measure of paper quality. We can also go further and estimate the impact of a journal or a particular researcher based on the number of citations. A generally accepted measure is the journal impact factor, which is simply the mean number of citations by paper published in the journal for some period in time. Individual researchers could be evaluated by the impact factor of the journals they published in, though it is considered as a bad practice. Another not-so-bad practise is h-index. By definition, one's h-index equals H if there are at least H citations of his top H papers1. It has also been criticised widely.

So, citation-based scoring has a lot of flaws. But what can we use instead? Another interesting approach is introduced by ReaderMeter. They collect the information about the number of people who have added some paper to their Mendeley collections and compute something like h-index. Unfortunately, they recently excluded some papers from their database, so the statistics became less representative but more accurate.

Co-authorship and Erdős number

Paul Erdős was a Hungarian mathematician who published about 14 hundred research papers with 511 different co-authors. That's why he has a special role in bibliometrics. Erdős number is defined as a collaborative distance from a researcher to Erdős. More strictly, Wikipedia defines:
Paul Erdős is the one person having an Erdős number of zero. For any author other than Erdős, if the lowest Erdős number of all of his coauthors is k, then the author's Erdős number is k + 1.
My Erdős number is at most 7 (via Olga Barinova, Pushmeet Kohli, Philip H.S. Torr, Bernhard Schölkopf, John Shawe Taylor, David Godsil). To be honest, Erdős number system is primarily used for math papers. Even if we use the wider definition, it is not that beautiful, because there are not too many gates, e.g. all the vision community will probably connect to Erdős via the machine learning gate. Thus, the researchers who work on the edge will be closer. To illustrate this fact, David Marr probably had not any finite Erdős number during his lifetime (now he definitely has). So, we can introduce, say, Zisserman number for computer vision.2 According to DBLP, Andrew Zisserman has 165 direct co-authors so far. Now, my Zisserman number is 3, David Marr's is 4 (via Tomaso Poggio, Lior Wolf, Yonatan Wexler).

The movie industry have their own metrics, which is the Bacon number (after Kevin Bacon). The distance is established 1 if two actors have appeared in a single movie. Someone tried to combine those two to the Erdős-Bacon number. For some person, it is just a sum of her Erdős and Bacon numbers. Of course, very few people have a finite Erdős-Bacon number, since one should both appear in a movie and publish a research paper (and it is still not sufficient). Often they are possessed by researchers who consulted the filming crew and accidentally were filmed. =) Erdős himself has this number 3 or 4 (depending on the details of definition), since he starred in N is a Number (1993) and his Bacon number is thus 3 0r 4.

The person with the probably lowest Erdős-Bacon number is Daniel Kleitman, an MIT mathematician, who has appeared in one of my favourite movies Good Will Hunting (1997) along with Minnie Driver, who collaborated with Bacon in Sleepers (1996). Since Kleitman has 6 joint papers with Erdős, his Erdős-Bacon number is 3.

Surprisingly, Marvin Minsky has his Erdős number (4) greater than his Bacon number (2), which he obtained via The Revenge of the Dead Indians (1993) and Yoko Ono. Another strange example is the paedophile's dream Natalie Portman. She has graduated from Harvard, saying she would rather "be smart than a movie star". That's my kind of a girl! A neuroscience paper [Baird et al., 2002] brought Natalie Hershlag (her real name) Erdős number of 5, and then she appeared in New York, I Love You (2009) along with Kevin Bacon, so she reached the same Erdős-Bacon number as Minsky, i.e. 6.

UPD (Mart 13, 2011). There is a totally relevant xkcd strip.

Scientific genealogy

Finally, I tell about what is known as scientific genealogy. Every grown-up researcher has a PhD advisor, usually one, while an advisor can have a lot of students. Let's just use the analogy with parents and children. We get a tree (or a forest) representing the historical structure of science. The mathematics genealogy project aims to recover this structure.

I tried to track my genealogy back. Unfortunately, I failed to find who was Yury M. Bayakovsky's PhD advisor. But if consider Olga Barinova my advisor, I am the 11th generation descendant of Carl Friedrich Gauß. Nice ancestry, huh?

The similar project exists for computer vision. There are 290 people in the base, though there are duplicates (I've found five Vitorio Ferrari's :). It seems strange that some trees have depth as big as 5 (e.g. Kristen Graumann and Adriana Quattoni are the 5th generation after David Marr), though vision is a relatively young field.

1 Okay, take the supremum of the set if you want to stay formal
2 It seems that Philip Torr has already used that number.

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ECCV 2010 highlights

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Today is the last day of ECCV 2010, so the best papers are already announced. Although I was not there, a lot of papers are available via cvpapers, so I eventually run into some of them.

Best papers

The full list of the conference awards is available here. The best paper is therefore "Graph Cut based Inference with Co-occurrence Statistics" by Lubor Ladicky, Chris Russell, Pushmeet Kohli, and Philip Torr. The second best is "Blocks World Revisited: Image Understanding Using Qualitative Geometry and Mechanics" by Abhinav Gupta, Alyosha Efros, and Martial Hebert. Two thoughts before starting reviewing them. First, the papers come from the two institutions which are (arguably) considered now the leading ones in the vision community: Microsoft Research and Carnegie-Mellon Robotics Institute. Second, both papers are about semantic segmentation (although the latter couples it with implicit geometry reconstruction); Vidit Jain already noted the acceptance bias in favour of the recognition papers.

Okay, the papers now. Ladicky et al. addressed the problem of global terms in the energy minimization for semantic segmentation. Specifically, their global term deals only with occurrences of object classes and invariant to how many connected components (i.e. objects) or individual pixels represent the class. Therefore, one cow on an image gives the same contribution to the global term as two cows, as well as one accidental pixel of a cow. The global term penalizes big quantity of different categories in the single image (the MDL prior), which is helpful when we are given a large set of possible class labels, and also penalizes the co-occurrence of the classes that are unlikely to come along with each other, like cows and sheep. Statistics is collected from the train set and defines if the co-occurrence of the certain pair of classes should be encouraged or penalized. Although the idea of incorporating co-occurrence to the energy function is not new [Torralba et al, 2003; Rabinovich et al., 2007], the authors claim that their method is the first one which simultaneously satisfy the four conditions: global energy minimization (implicit global term rather than a multi-stage heuristic process), invariance to the structure of classes (see above), efficiency (not to make the model order of magnitude larger) and parsimony (MDL prior, see above).

How do the authors minimize the energy? They restrict the global term to the function of the set of classes represented in the image, which is monotonic w.r.t. argument set enclosing (more classes, more penalty). Then the authors introduce some fake nodes for αβ-swap or α-expansion procedure, so that the optimized energy remains submodular. It is really similar to how they applied graph-cut based techniques for minimizing energy with higher-order cliques [Kohli, Ladicky and Torr, 2009]. So when you face some non-local terms in the energy, you can try something similar.

What are the shortcomings of the method? It would be great to penalize objects in addition to classes. First, local interactions are taken into account, as well as global ones. But what about the medium level? Shape of an object, size, colour consistency etc. are the great cues. Second, on the global level only inter-class co-occurrences play a role, but what about the intra-class ones? It is impossible to have two suns in a photo, but it is likely to meet several pedestrians walking along the street. It is actually done by Desai et al. [2009] for object detection.

The second paper is by Gupta et al., who have remembered the romantic period of computer vision, when the scenes composed of perfect geometrical shapes were reconstructed successfully. They address the problem of 3D reconstruction by a single image, like in auto pop-up [Hoiem, Efros and Hebert, 2005]. They compare the result of auto pop-up with Potemkin villages: "there is nothing behind the pretty façade." (I believe this comparison is the contribution of the second author). Instead of surfaces, they fit boxes into the image, which allows them to put a wider range of constraints to the 3D structure, including:
  • static equilibrium: it seems that the only property they check here is that centroid is projected into the figure bearing;
  • enough support force: they estimate density (light -- vegetation, medium -- human, heavy -- buildings) and say that it is unlikely that building is build on the tree;
  • volume constraint: boxes cannot intersect;
  • depth ordering: backprojecting the result to the image plane should correspond to what we see on the image.
This is a great paper that exploits Newtonian mechanics as well as human intuition, however, there are still some heuristics (like the density of a human) which could probably be generalized out. It seems that this approach has a big potential, so it might became the seminal paper for the new direction. Composing recognition with geometry reconstruction is quite trendy now, and this method is ideologically simple but effective. There are a lot of examples how the algorithm works on the project page.

Funny papers

There are a couple of ECCV papers which have fancy titles. The first one is "Being John Malkovich" by Ira Kemelmacher-Shlizerman, Aditya Sankar, Eli Shechtman, and Steve Seitz from the University of Washington GRAIL. If you've seen the movie, you can guess what is the article about. Given the video of someone pulling faces, the algorithm transforms it to the video of John Malkovich making similar faces. "Ever wanted to be someone else? Now you can." In contrast to the movie, in the paper not necessarily John Malkovich plays himself: it could be George Bush, Cameron Diaz, John Clooney and even any person for whom you can find a sufficient video or photo database! You can see the video of the real-time puppetry on the project page, although obvious lags take place and the result is still far from being perfect.

Another fancy title is "Building Rome on a Cloudless Day". There are 11 (eleven) authors contributing to the paper, including Marc Pollefeys. This summer I spent one cloudless day in Rome, and, to be honest, it was not that pleasant. So, why is the paper called this way then? The paper refers to another one: "Building Rome in a Day" from ICCV 2009 by the guys from Washington again, which itself refers to the proverb "Rome was not built in a day." In this paper authors build a dense 3D model of some Rome sights using a set of Flickr photos tagged "Rome" or "Roma". Returning back to the monument of collective intelligence from ECCV2010, they did the same, but without cloud computing, that's why the day is cloudless now. S.P.Q.R.

I cannot avoid to mention here the following papers, although they are not from ECCV. Probably the most popular CVPR 2010 paper is "Food Recognition Using Statistics of Pairwise Local Features" by Shulin Yang, Mei Chen, Dean Pomerleau, Rahul Sukthankar. The first page of the paper contains the motivation picture with a hamburger, and it looks pretty funny. They insist that the stuff from McDonald's is very different from that from Burger King, and it is really important to recognize them to keep track of the calories. Well, the authors don't look overweight, so the method should work.

The last paper in this section is "Paper Gestalt" by the imaginary Carven von Bearnensquash, published in Secret Proceedings of Computer Vision and Pattern Recognition (CVPR), 2010. The authors (presumably from UCSD) make fun of the way we usually write computer vision papers assuming that some features might convince a reviewer to accept or reject the paper, like mathematical formulas that create an illusion of author qualification (although if they are irrelevant), ROC curves etc. It also derides the attempts to apply black-box machine-learning techniques without the appropriate analysis of the possible features. Now I am trying to subscribe to the Journal of Machine Learning Gossip.

Colleagues

There was only one paper from our lab at the conference: "Geometric Image Parsing in Man-Made Environments" by Olga Barinova and Elena Tretiak (in co-authorship with Victor Lempitsky and Pushmeet Kohli). The scheme similar to the image parsing framework [Tu et al., 2005] is utilized, i.e. top-down analysis is performed. They detect parallel lines (like edges of buildings and windows), their vanishing points and the zenith jointly, using a witty graphical model. The approach is claimed to be robust to the clutter in the edge map.

Indeed, this paper could not have been possible without me. =) It was me who convinced Lena to join the lab two years ago (actually, it was more like convincing her not to apply for the other lab). So, the lab will remember me at least as a decent selectioner/scout...

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Web-scale Content Based Image Retrieval

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Do you remember the concept of Computer Blindness? It is about people intuitively expecting from computer vision algorithms results unreachable by the state-of-the-art methods. Believe or not, recently I fell for that trick too.

I was looking throw Navneet Dalal's slides on Histograms of Oriented Gradients. They contained a lot of frames captured from the movies as examples. There was the following one among them:



It seemed familiar to me. I endeavoured to remember the movie, but I failed.1 So I decided to check out some web-sites that offered the inverse image retrieval.

Sure, first I turned to St. Google. The similar image service disappointed me since it was not able to find the similar image of what I want. Actually, it has some indexed base (not very large), and one can find the similar images only within that base. If one somehow find the image from the base (e.g. using a text query), (s)he is shown a button that allows similar image retrieval.

There are different web-sites for this purpose. TinEye positions itself as a reverse image search engine. However, it failed to find anything. It honestly admitted that nothing similar was found. GazoPa found something, but that was different from what I had expected, though the found images were similar in saturation. It was strange because usually such methods work on greyscale images to be robust to the colour levels shifts.

Then I decided to check if the situation is common, and tried a different image, which I found on my desktop:2


I wanted to find the name of its author and the title3. The result was almost the same. TinEye found nothing, GazoPa found a lot of pictures of different girls.

Why is the web-scale CBIR not possible to date? Because there are simply a lot of images in the web, and it is intractable to perform search in such a large index. The common workflow implies feature extraction and further feature matching. From each image hundreds of features could be extracted. Each feature is a high-dimension vector (e.g. 128D). Suppose we have N images in the index. If we extract 100 features from each (which is fairly the lower bound), to handle the given picture we should match 100 * 100 * N features in 128D space. It is really hard to do it instantly even if N is small. In 128D, indexing structures like kd-trees do not improve performance over exhaustive search, because branch and bound method is unable to reduce the search space in practice (approximate methods partly solve the problem). For example, it took 13 hours to match 150,000 photos on 496 processor cores for the Building Rome in a Day project. This also tells us that there are 150K photos of Rome in the web, so try to imagine the whole number of pictures!

But there is a certain hope for success in the web-scale CBIR. First, when we deal with billions of photos (and trillions of features) kd-trees are likely to give sublinear performance. Second, one could use the web context of an image to seed out come obviously wrong matches.
In the long run, we need to develop more descriptive image representation and learn how to combine the textual content with the content. Also, using the user interest prior could be useful. The engine may start the search from the most popular photos and ignore the least popular ones. Thus, the task could be formulated as a sequential test, where the predictor should be able to say if the found match is good enough, or it should continue search.

UPD (Apr 7, 2010). Here is a rather broad list of visual image search engines.

1 The first thought was about Roman Polanski's "The Pianist", which is surely wrong. If you recognize the movie please let me know!

2 I've seen the picture in the Hermitage Museum and downloaded it after returning from St. Petersburg, because the girl had reminded me my friend Sveta.

3 It is actually Franz von Lenbach's Portrait of Marion Lenbach, his daughter.

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Computer Vision: Fact & Fiction

Posted in Labels: , , , , , | at 14:30

I was surfing the web today and came upon Stanford CS 223B course (Introduction to Computer Vision), which is said to be fucking hard. The first course homework is to watch the series of films "Computer Vision: Fact & Fiction" where computer vision stars (like David Forsyth and Andrew Zisserman) analyse computer vision technologies featured in Hollywood movies. The videos require no background in Vision and might be interesting to everyone. To me, it is also interesting to see how the famous vision folks look and talk.


My friend Tolya Yudanov spoke about that to talk about realistic in The Terminator movie is like "arguing about physical correctness of animé. Terminator is the complex AI of the future, and it is stupid to apply modern computer vision criteria to it." So, it is a good illustration of the concept of computer blindness. I encourage you to watch the videos, they are worth watching.

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