ICVSS 2010: some trivia

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If you think that summer schools are only about lectures and posters, you are certainly wrong. Well, I also did not expect that I would get drunk every night (it was not in Russia after all!). On the first night I convinced people to go to the beach for night swimming, but on the next day I realized that it was not a really good idea, because lectures started at 9. Eventually, we did not go to bed early for the rest of the week.

I would like to thank "Marsa Sicla" people (Fig. 1), you were the great company!

Fig. 1. The Marsa Sicla company. Left to right: Richard, Ramin,
Nicolas, Xi, Vijay, Clément, Aish, me. Sandra is behind the camera.

Actually, there was a reason why we had such a close-knit company. On the Fig. 2 you can see the map of the region where the school took place. The school was hosted by Baia Samuele hotel village (inside the blue frame on the map). The cheapest accommodation at Baia Samuele costed €100 per night, so some pragmatic people scared a different accommodation up. It was in a neighbouring hotel village, Marsa Sicla.


Fig. 2. ICVSS location map. View ICVSS_2010_MSvsBS in a larger map

In a map it looked pretty close, but in practice it turned out that there were a lot of fences (blue line)! Officially, we had to walk along the red route (~2.5 km, partly along a highway) through the official entrance. Moreover, we had to leave the village for lunch (since there were buffet), otherwise we would have been charged €30 per meal. To control it they wanted us to leave our documents at the control post (bottom blue tick on Fig. 2). Thus, we were supposed to walk 2.5 km four times a day. But we found a better solution.

Although the fence was equipped with a barbwire, we managed to find two shortcuts (green ticks). One of them led through ever-closed gate (Fig. 3), which were suitable for hopping over. So, we used the green path usually. It made the way two times shorter. Moreover, we did not have to leave Baia Samuele for lunch, since we weren't officially there. Sometimes we even had free lunch, that finally proved that the no free lunch theorem is wrong (joke by Ramin).

Fig. 3. Hopping over the fence.

Well, the lunch was really great. But this was not really a question of food, the main reason not to leave Baia Samuele was of course socialization, which used to be active during lunch (and also Internet access at the conference centre :). If we knew about such situation before booking the apartments, we would probably followed the official accommodation recommendations. However, we had a great company at Marsa Sicla too. But because of "unofficial night programme" I had to (almost) sleep during lectures. Most of guys was on their last year of PhD, so they considered the school as a vacation. But I really wanted to learn a lot of stuff. It was really difficult to perceive any information when you had not slept enough time. Surprisingly, I somehow managed to pass the final exam, but I feel I need to look through the lecture slides again, reading up some papers they refer to, otherwise the scientific part of the school will be useless for me.

Thus, I finish posting about ICVSS. To conclude, I want to recommend everyone to visit such summer schools, because they are the best way to enter into the community.

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ICVSS 2010: Have you cited Lagrange?

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Yeah, I have. :) Before the beginning of the summer school we received a homework assignment from Prof. Stefano Soatto. We had to read the three papers and discover the roots of ideas exploited by the authors. Such connections are not obligatory expressed by references. As a result, one had to get a tree (or, may be, lattice) with a root in the given paper. Since the task was not accurately formulated, most of us chose just one paper and made a tree for it. Later it turned out that all three problems (optical flow estimation, multi-view stereo and volume registration) have the same roots because they lead to the similar optimization problems and we were supposed to establish that connection.

I (like most of the students) had a limited time to prepare the work. All the modern research in optical flow is based on two seminal papers by MIT's Horn & Shunk and CMU's Lukas & Kanade, both from 1981. During the last 30 years a lot of work has been done. It was summarized by Sun et al. (2010), who discovered that only small fraction of the ideas give significant improvement and combined them into quite simple method that found its place on the top of Middlebury table. Since I had no time to read a lot of optical flow papers, I discovered a lot of math stuff (like PDEs and variation calculus) used in Horn-Shunk paper. Just as a joke I added references to the works of Newton, Lagrange and d'Alambert to my report. Surprisingly, joke was not really understood.

There were only 21 submissions, one of them was 120 pages long (the author did not show up at the seminar =), the tree depth varied from 1 to 20. I was not the only one who cited "pre-historic" papers, someone traced ideas back to Aristotle through Occam. The questions about the horizon arose: it is really ridiculous to find the roots of computer vision at Aristotle works. Since the prizes were promised for rare references (the prize policy was left unclear too), some argument took place. Soatto did not give any additional explanations on the formulation of the task but let the audience decide which references are legitimate in controversial cases. Eventually, I was among the 5 people whose references were considered controversial, so I needed to defend them. Well, I suppose I looked pretty pathetic talking about Newton's finite differences which were used for approximation of derivatives in the Horn-Shunk's paper. Surprisingly, almost a half of the audience voted for me. =) Also, my reference to the Thomas Reid's essay was tentatively approved.

Finally, there were no bonuses for the papers that could not be found with Google, and the whole $1000 prize went to the Cambridge group. To conclude, Soatto (among other) said that nobody had read any German or Russian papers. After the seminar I told him how I tried to dig into the library (it is described here in Russian), he answered something like "funny, it is hard to find them even for you!"

One evening during the dinner we talked about Russia, and Vijay told a lot of interesting stuff (like the guy who developed ChatRoulette is now working in Silicon Valley). He remembered Stephen Boyd who is known for his jokes about Russia. I said that I watched the records of his amazing course on Convex Optimization. It turned out that Vijay (who is now a PhD student in Stanford) took that course, and he promised to tell Boyd that he has a fan in Russia. (Or, maybe in Soviet Russia fans have Boyd =).

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ICVSS 2010: lectures and posters

Posted in Labels: , , , , , , , , , , | at 03:17

I returned from my eurotrip yesterday and now I am ready to start a series of posts about International Computer Vision Summer School (ICVSS 2010). Generally, I enjoyed the school. That week gave me (I hope) a lot of new knowledge and new friends.

The scientific programme of the school included lectures, tutorials, a student poster session and a reading group. Lectures occupied most of official programme time and were given by a great team of professors including Richard Szeliski, renowned Tomasso Poggio and enchanting Kristen Grauman. I am not going to describe all the talks, but feature the ones that are close to my interests. You can find the complete programme here. Unfortunately, no video was recorded, but I have an access to all the slides and posters, so if you are interested in anything, I can send it to you (I believe it does not violate any copyrights).

Wednesday was the day of Recognition. Kristen Grauman gave a talk about visual search. She covered the topics concerning specific object search using local descriptors and bags of words, object category search with pyramid matching, and also discussed state-of-the-art in the challenging problem of web-scale image retrieval. Mark Everingham continued with a talk about category recognition and localization using machine learning techniques. Localization is reached using bounding boxes, segments, or object parts search (like finding eyes and a mouth to find a face). Sure he could not avoid to mention importance of context. He also explained the PASCAL VOC evaluation protocol.

The tutorials covered some applications and did not really impress me. Poster session was a great opportunity to meet people. Some posters were really decent, for example Michael Bleyer's poster on dense stereo estimation using soft segmentation, which won a half of the school's best presentation prize. The work was done with Microsoft Research Cambridge, they formulated a really complicated energy function based on surface plane estimations and minimized it with Lempitsky's graph-cut based fusion-move algorithm (2009). More details could be found in their recent CVPR paper.

The problem with the poster session was that lecturers did not attend it, although they could really give a great feedback. My presentation was in the last day of the session after the not-really-popular reading group and in the room upstairs (its existence was not a well-known fact :), so many people preferred to spend that evening on the beach. The school audience was quite heterogeneous, there were a lot of people from medical imaging, video compression etc., so I had to explain some basics (like MRFs) to some students interested in my poster. There were also really smart guys (like those from Cambridge group). There was a bit of useful feedback: someone recommended me to use QPBO for inference, and I should probably consider it.

Since the speakers did not attend the poster session, students had to communicate with them informally. A roommate of mine, Ramin, who does a crowd analysis, caught Richard Szeliski and asked him about local descriptors in video that operate in 3D image-time space. Szeliski told that it is a promising field and even remembered Ivan Laptev's name. During our tour to Ragusa Ibla I asked Mark Everingham (who is probably the closest of all speakers to my topic) about 3D point cloud classification. He said it was not really his field, but it should be fruitful to analyse clouds not only in local levels, and stuff like multi-layer CRFs could be useful. During the last 2 years there appeared a few papers that exploit that simple intuitive idea and incorporate shape detectors with CRFs, but it usually looks awkward. Well, may be smartly designed multi-layer CRFs might be really useful. It is funny, when I told Everingham that I'm from Moscow he replied that it was great, Moscow has a great math school and remembered Vladimir Kolmogorov. So, our education seems to be not so terrible. :D

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Mendeley news

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Few months ago I posted about Mendeley, the reference management system. I've been using it all the time and I am really satisfied. It is much more convenient to have your papers on a server than on a flash drive. Also keeping meta-information about the papers makes dealing with them (exploring, reading, citing etc.) more pleasant.

In this month a major update was released and a monetizing scheme was introduced. There are now 3 tariff plans in addition to the free one that allows one to use up to 1Gb of server disk space. There are also restrictions on the max number of shared collections and the number of users per collection. It does not look critical after all: there are no features you cannot use in the free version, so the company seems not evil. However, on the sister project last.fm the radio feature became paid after years of free service, which caused many users drift to grooveshark.

The update has affected the design of the paper info right-hand bar, and the references bar was eliminated. They promise to rebirth the bar in the future releases. It was really unusable before. When you read a paper, it is handy to have the list of indexed references on the bar, especially if the reference-style citing is used (it is the major problem with reading papers from the screen: where the hell [42] is referencing to?) I hope the people in Mendeley realize it.

It would be great to have a possibility to find papers without leaving the application. In theory, you could drag a citation from the references bar to a collection, find its (more) precise details via Internet search, and you are likely to have the link, and if it is direct, you can add the pdf with the Add File dialog using the found URL. In practice, the found URLs usually are not direct, they refer to IEEE/ACM/Springer pages, where the paper could be downloaded (usually not freely). In the same time, the paper is likely to be available for free through the direct link on the author's homepage. Moreover, Google Scholar often finds them too, but Mendeley chooses indirect links.

Mendeley needs to be more "semantic" while working with authors and conferences. It stores the author name as it appears in the paper. When you want to filter your papers by author, you can see "Shapovalov, R", "Shapovalov, R.", "Shapovalov, Roman", "Shapovalov, Roman V." etc. in the list. There are bases like DBLP, Mendeley can build their own index, so the author should be identified. If we know the author (but not only her name appearance in the paper), we are able to filter Google Scholar results and retrieve the direct link to the paper hosted on the author/university site. Finally, it is feasible to create the system, where human don't need to find papers. If I see the reference, I can automatically retrieve the pdf in a few seconds. I want Mendeley to develop in this direction.

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Object detection vs. Semantic segmentation

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Recently I realized that object class detection and semantic segmentation are the two different ways to solve the recognition task. Although the approaches look very similar, methods vary significantly on the higher level (and sometimes on the lower level too). Let me first state the problem formulations.

Semantic segmentation (or pixel classification) associates one of the pre-defined class labels to each pixel. The input image is divided into the regions, which correspond to the objects of the scene or "stuff" (in terms of Heitz and Koller (2008)). In the simplest case pixels are classified w.r.t. their local features, such as colour and/or texture features (Shotton et al., 2006). Markov Random Fields could be used to incorporate inter-pixel relations.

Object detection addresses the problem of localization of objects of the certain classes. Minimum bounding rectangles (MBRs) of the objects are the ideal output. The simplest approach here is to use a sliding window of varying size and classify sub-images defined by the window. Usually, neighbouring windows have similar features, so each object is likely to be alarmed by several windows. Since multiple/wrong detections are not desirable, non-maximum suppression (NMS) is used. In PASCAL VOC contest an object is considered detected, if the true and found rectangles are intersected on at least half of their union area. In the Marr prize winning paper by Desai et al. (2009) more intelligent scheme for NMS and incorporation of context is proposed. In the recent paper by Alexe the objectness measure for a sliding window is presented.

In theory, the two problems are almost equivalent. Object detection reduces easily to semantic segmentation. If we have a segmentation output, we just need to retain object classes (or discard the "stuff" classes) and take MBRs of regions. The contrary is more difficult. Actually, all the stuff turns into the background class. All the found objects within the rectangles should be segmented, but it is a solvable issue since foreground extraction techniques like GrabCut could be applied. So, there are technical difficulties which could be overcome and the two problems could be considered equivalent, however, in practice the approaches are different.

There arise two questions:
1. Which task has more applications? I think we do not generally need to classify background into e.g. ground and sky (unless we are programming an autonomous robot), we are interested in finding objects more. Do we often need to obtain the exact object boundary?
2. Which task is sufficient for the "retrieval" stage of the intelligent vision system in the philosophical sense? I.e. which task is more suitable for solving the global problem of exhaustive scene analysis?

Thoughts?

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Max-product optimization methods and software

Posted in Labels: , , , , , , , , , | at 22:38

I hoped I would never post a sorry-for-not-posting message, but I do (and now I feel like I owe you a long post =). In fact, I have been being really busy, particularly with putting my masters thesis down to paper. However, it has a positive outcome: I systematized my knowledge about inference techniques in MRFs, and I'd like to share them here.

Please don't consider it a survey. While it claims to be quite comprehensive, it is not enough deep and verbose. You are not gonna see a lot of formulas, but the links to papers and implementations along with the recommendations on applicability of the methods and the software. It could be useful for anybody who wants to quickly try energy minimization in her task, but don't wanna spend a lot of time digging into the literature and implementing algorithms. So let's call this form of posting unsurvey. You can always find more details on the topic in Stan Li's book (not to be confused with Stan Lee recently appeared in The Big Bang Theory).

Max-product energy minimization problem arises in a number of fields such as statistical physics, social network analysis, and, of course, computer vision, where the problems like image denoising, dense stereo reconstruction, semantic segmentation lead to the single discrete optimization problem: maximize the product of some functions defined over nodes and edges of the graph G=(N,E), typically a grid over image pixels in low-level vision. Due to using logarithms, it is equivalent to maximizing the sum


where each Yi corresponds to a node of the graph and takes on a value from a fixed discrete range. It could be a class label in semantic segmentation, or a disparity value in the pixel in dense stereo. So-called potential functions φ (unary in the first term and pairwise in the second one) define possibility of the assignment given class labels to the nodes and edges correspondingly. They could be conditioned by the data. For example, unary potentials could reflect colour information in the pixel, and pairwise potentials could enforce assignment of the similar labels to the ends of an edge to provide smoothness in the resulting labelling. If the graph contains cliques of the order 3 and greater, one should consider more terms (as I explained earlier), but in practice higher order cliques are often ignored. Also, an important particular case, 4-connected grid, contains only second order cliques, that's why the research community focused on this "pairwise" problem.

In general, this maximization problem is NP-hard. That's why in the 1980s they solved it using genetic algorithms or simulated annealing. Fortunately, specific methods have been developed.

First of all, there are two cases when the exact global optimum could be found in polynomial time. The first is when the graph has a tree structure. Actually, I don't know application where this case is useful. Probably, it is good for analysis of parsing trees in natural language processing. In this case the maximum is found using the dynamic programming based belief propagation algorithm. One node is selected as the root, and messages are passed from the root and then back to the root. The optimal assignment is thus found.

Another polynomial case allows an arbitrary graph topology, but restricts the number of labels and the form of pairwise potentials. Only one of the two labels could be assigned to each node. Pairwise potentials should be submodular, i.e. φ(0,0) + φ(1,1) >= φ(0,1) + φ(1,0). Thus, the binary assignment implies that all the nodes should be divided into the two groups according to their labels. This division is found using the algorithm for finding minimum cut on the supplementary graph. Two fake nodes are added to the graph and declared as the source and the sink. Both fake nodes are adjacent to all the nodes of the "old" graph. Using your favourite min-cut/max-flow algorithm you can separate the nodes into two sets, which gives you the resulting labelling.

Unfortunately, the binary labelling problem is not so common too. In practice people want to choose one of the multiple labels for the nodes. This problem is NP-hard, but the iterative procedures of α-expansion and αβ-swap give good approximations with certain theoretical guarantees. [Boykov et al., 2001] During each iteration the min-cut algorithm is run, so there is an important constraint. Each projection to any two variables of pairwise potentials should be submodular. If it is hold for your energy, I recommend you to use this method since it is really fast and accurate. The implementation by Olga Veksler is available.

What should we do if the energy is not submodular and the graph contains cycles? There are some methods too, but they are not so fast and typically not so effective. First of all, the stated integer programming problem could be relaxed to linear programming. You can use any LP solver, but it will eventually take days to find the optimum. Moreover, it won't be exact, because the relaxed problem solution could be rounded back in multiple ways. No surprise here, because the possibility of getting the global optimum would prove that P = NP.

One of the methods for approaching the general problem is Loopy Belief Propagation [Kschischang, 2001]. No doubt, it is really loopy. Dynamic programming is used on the graph with loops, and it can eventually fall into the endless loop passing some messages along the loop. It is now considered outdated, but you can still try it, it is implemented in libDAI. The input is a factor graph, which is inconvenient in most cases, but really flexible (you could create cliques of arbitrary order).

State-of-the-art approach in general case, tree-reweighted message passing (TRW), has been developed by Wainwright and Kolmogorov. [Kolmogorov, 2006] It is based on the decomposition of the original graph to some graphs where exact message-passing inference is possible, i.e. trees. Message passing is done iteratively on the trees, and trees are enforced to assign the same values in particular nodes. The procedure is guaranteed to converge, but unfortunately not always to the global maximum. However, it is better than Loopy BP. Kolmogorov's implementation contains LBP and TRW-S under a common interface, so you can compare their performance. To understand the decomposition technique better, you could read Komodakis's paper [2007]. It describes a general decomposition framework.

Once a company of the world-leading MRF researchers gathered and implemented LBP, graph-cuts and TRW-S under common interface, investigated performance and even shared their code on a Middlebury college page. It would be great to use those implementations interchangeably, but unfortunately the interface is tailored for the stereo problem, i.e. it is impossible to use the general form of potentials. My latest research is about usage MRFs for laser scan classification, where the topology of the graph is far from a rectangular grid, and the potentials are more complex than the ones in the Potts model. So I ended up in writing my own wrappers.

Another interesting method was re-discovered by Kolmogorov and Rother [2007]. It is known as quadratic pseudo-boolean optimization (QPBO). This graph-cut based method can either assign a label to a node, or reject to classify it. The consistency property is guaranteed: if the label has been assigned, this exact label is assigned in the global optimum. The equivalent solution could be obtained using a specific LP relaxation with 1/2 probabilities for class labels allowed. I have not used the technique, so I cannot recommend an implementation.

To summarize, the algorithm for choosing an optimization method is the following. If your graph is actually a tree, use belief propagation. Otherwise, if your energy is submodular, use graph-cut based inference; it is both effective and efficient (even exact in the binary case). If not, use TRW-S, it often yields a good approximation.

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On Fundamental Artificial Intelligence

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Today I attended Mikhail Burtsev's lecture on evolutionary approach to AI. The lecture was hosted by Moscow Polytechnic Museum's lecture hall. First of all, the organization was great, and the quality of presentation was high. Unsurprisingly, the hall was almost totally occupied by the listeners. I recommend you to attend their lectures.

Mikhail talked a lot about the history and foundations of AI, which was not new to me. He did not bore the audience with the psychology-related stuff, but featured two approaches to AI, that have been being developed simultaneously all the way since 1950s: artificial neural networks and symbolic computational AI. ANNs attempt to model human brain functioning by means of artificial neurons and axons. The problem here is if we build even human-scale artificial brain (which was actually done in Allen Institute for Brain Science in 2005), it will be isolated from the world, and could not be trained like real human brain. Symbolic AI uses formal logic to solve the problems. It also suffers certain problems, like the lack of uncertainty. So, vanilla AI is in the period of stagnation now (it dates bake to the late 1980s, when Rodney Brooks started to criticize it).

Mikhail's point was the one that most researchers attempt to build a straight away model of human intelligence. He suggested to look at the evolution process. We need to start with understanding how the neural system of an amoeba operates, and then track the evolution of it to find out how a human being's mind works. Evolutionary algorithms might be useful there.

I cannot agree with that point. Last 50 years witnessed the failure of fundamental AI research. Their common problem was they tried to model some cognitive mechanisms instead of tackling actual problems, while those problems were being solved by different techniques. Consider the autonomous robot control problem (e.g. a robotic vacuum cleaner). It can be formulated in terms of reinforcement learning and solved by the methods not connected to the vanilla AI (dynamic programming-based, I suppose). Mikhail does not depart from the problem, again. So I don't really believe in success of the method. I am also sceptic to the computational methods inspired by biology. They could be a good starting point, but they used to be supplanted by mathematically founded ones.

Another issue raised in the discussion after the lecture was AI safety, which is probably the most popular philosophical question connected to AI. As far as I understood, there are two main concerns: the AI could become self-replicated (or self-improved) uncontrollably, and it could become self-motivated. If the AI is self-replicated, it is not so dangerous unless it is self-motivated. If it is motivated by a human, it is bad just like nuclear weapon, which is scary only if an evil party possesses it (so, nothing brand new here). If the AI will be motivated to gain the world domination, it could be harmful. But where such self-motivation can come from? In my opinion, the only option is the biological obsession to propagate (imagine some joint AI system with bio-motivator), but such a disaster has already been overcome once. Do you remember that rabbits in Australia? I hope, an uncontrolled AI won't be a bigger problem.

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ICVSS 2010

Posted in Labels: , , , , , , | at 22:16

This summer I'm going to take part at the International Computer Vision Summer School. I have already been accepted and registered for it. I'm really looking forward this event and hope it will not be too hot in July in Sicily. =)

I am now seeking for a room-mate, because accommodation is quite expensive there. If you are a participant too and have a similar problem, please feel free to contact me!

One of the activities of the school is a reading group leaded by Prof. Stefano Soatto. As a homework, we need to investigate the reference trees on three certain subjects. Moreover, there will be prizes for students who will have discovered papers that could not be found via standard search engines. I suppose I have a bonus here: Russian science has been isolated from the, well, science due to that iron curtain thing (it is still quite isolated, by inertia). So, I just need to dig in a library and find something appropriate in old Soviet journals.

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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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Olga's CVPR paper

Posted in Labels: , , , , , , | at 10:33

I'd like to congratulate my colleague Olga Barinova on the paper, which is accepted to CVPR international conference! To my knowledge, it is the first paper from our lab, which is accepted to a Rank 1 conference (though it was written during Olga's internship at Microsoft Research).

The paper is on multiple object retrieval. They extend the Hough transform with some graphical model, which makes the results more robust. As soon as the paper become publicly available, I'll add the link here.


UPD. (Apr 20, 2010) PDF

UPD (Jul 30, 2010) Video

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