Computer Blindness

Last week Google introduced Search by Image feature. There were a handful of web-sites that suggested content-based image retrieval in the Internet, but the quality was low, as I blogged earlier. I repeated the queries TinEye failed at, and Google image search found them both! It found few instances of Marion Lenbach (one of them from this blog, which means the coverage is large!), and I finally remembered the movie from the HOG paper: The Talanted Mr. Ripley.

So, from the quick glance Google finally accomplished what the others could not do for ages. Why did they succeed? There are two possible reasons: large facilities that allow building and storing large index efficiently, and a unique technology. The former is surely the case: it seems the engine indexed a large portion of the photos in the web. I cannot say anything about the technology: there is nothing about that among the Google's CVPR papers, so one need to do black-box testing to see which transformations and modifications are allowed.

Google seems to expand to the areas of multimedia web, even where the niche is already occupied. Recently they announced their alternative to grooveshark, the recommendation system for Music beta (the service is unfortunately available on invitation basis in the US only). The system is not based on collaborative filtering (only), they (also) analyse the content. I planned to investigate this area too, but given that it is becoming mature and thus not so alluring. I am eager to see if the service will succeed. After all, Buzz did not replace Twitter, and Orkut did not replace Facebook.

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.

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.

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:

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.

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.

Last summer, during my internship at CMP in Prague I drew some xkcd-style comic strips, and I want to share some of them here. I considered starting a different blog for that, but there are too few of them, and they do suck (at least in comparison to Randall's work). However, most of them are programming/research related, so they are appropriate here.

I planned to use them when I would have nothing to post about. This is not the case now (I am planning a couple more posts in a week or so), but this one is topical. It was originally drawn in September 1, 2009 when GMail was not operating for a few hours. From the recent news: Google shuts Google Wave down, admitting their failure predicted by some sceptics (see the image title text).

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.

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

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

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.

The project is based in London, and share some management with Last.fm. Unsurprisingly, they have a social network upon all that. You can share you paper collections, upload your articles, synchronize your library with Mendeley Web. A user has up to 500 Mb space on servers. Now I see, where the idea of SciPeople, Russian scientific network, comes from. I don't know if such networks are the future of science, but they are, however, worth paying attention.

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