Some publications in 2013

The year is beginning with a small batch of publications on the different topics I'm working on.

Two years ago, we developed a new signature for image retrieval and classification based on tensors aggregation we named VLAT. The paper giving the very details of the method (plus a bonus for cheap large scale computation) has now been published in Computer Vision and Image Understanding at Elsevier. In the meantime, Romain Negrel (Ph.D. Student) has completely redesigned the method to improve its effectiveness. His work has now been accepted in IEEE Multimedia. There are some nice experiments in this paper, including large scale retrieval (1M images) at very low bitrate (less than 64 bytes per image).

On the video front, we have a paper accepted at MVA 2013 with Olivier Kihl (PostDoc), on video descriptors using polynomials expansion. We have very good results on well known data-sets, which makes me think this approach sounds very promising.

In 3D object retreival, we have an accepted paper at 3DOR with Hedi Tabia. This was a pretty straight forward extension of our still images indexing methods to 3D Objects, and it works well.

On a totally different topic, I recently did a paper with my colleague Aymeric Histace on the modeling of an insect (the bark beetle) using a multi-agents system. This was something I haven't done for years, and it was fun to do. The novelty in our approach is that we consider the chemical markers released by the agents and the environment to evolved thanks to a partial differential equations system modeling the physical spreading. This concurrent evolution between MAS and PDE makes the behavior of the agents a lot less predictable. This work was in part done by Marie-Charlotte Desseroit (undergrad student) during an internship last summer, which I find pretty impressive.

Paper accepted at ICPR 2012

We have a paper on image categorization accepted at ICPR next November. This is the other part of the Work Romain Negrel has been doing with VLAT. This time it's about efficiency in image classification. We tried to put every tricks of latest image categorization techniques (like dense sampling, spatial pyramid, and so on) into our VLAT while still retaining small size signatures.

All in all, we managed to achieve 61.5% mAP on VOC2007, which is not bad at all considering we used a single feature and a linear classifier (a stochastic gradient descent from Leon Bottou). Actually, if you put the throttle a bit further, you can expect better results, but then it becomes very heavy computationally speaking. As usual, some code is available here, although it's only for the Holydays dataset right now. At least you can produce the features and then use your own machine learning library (or mine, of course!).

Paper accepted at ICIP 2012

We have a paper accepted at ICIP next september. This is the work Romain Negrel has beeing doing on trying to reduce the size of our VLAT features using some kernel based dimensionality reduction techniques. We focused on search by similarity, and it happened to give very comparable results for large scale benchmarks.

I hope to release some code on this very soon. At least you can check this page

I'll bee in Brugge next week for the ESANN conference.

Elsevier, sometimes I hate you

On Friday morning, I received a nice email from Elsevier stating that, finally, one of my articles will be published. I don't really know what is the more irritating: The fact that it was accepted 2 years ago for publication, or the big spam at the end of the email promoting some totally stupid and useless stuff like certificates of publication.

Come on, Elsevier. I wrote this article back in 2009, and the peer review went fine so that it was accepted in early 2010. What is wrong with your publishing processes? Two years to get from the editor's desk to the printer is a bit long, isn't it? It is somehow disappointing to wait that much time.

Moreover, why would you think I would be interested in stupid gifts like a giant poster version of the first page, or a certificate of publication (at 35 euros each)? Sure I am proud of the work I did on this. But I am proud of the work in itself, not the way it is published. The fact that it took the shape of a journal article published by Elsevier rather than a technical report is just the same to me.

This job is sufficiently hard enough with all the bibliometric shit around, that I don't need some corporate consumerism on top of it. Now, please, just get off my lawn.

Paper accepted at ESANN 2012

We (N. Thome, M. Cord, A. Rakotomamonjy and me) received the notification of acceptance for a poster presentation at ESANN 2012. This work is on learning product combinations of kernels.

The sketch is as follows: suppose you have several types of features and signatures leading to a variety of kernels (typically Gaussian kernels). This is quiet a common scheme in Computer Vision. You might want to combine them, and usually people use MKL approaches (i.e. a weighted sum of kernel). However, in most cases these kernels are redundant, and you would better do a product combination of these (think of the different scales in Spatial Pyramid or different scales of the same descriptors). The product is like an 'AND' gate while the sum more like an 'OR' gate, thus if your features are redundant, the product is more likely to denoise than the sum.

The bad thing about this product counterpart of MKL is that it is non-convex (we have a nice proof about this). So we proposed and algorithm finding a local optimum. While this might not be the best combination possible, it is sufficiently robust in practice to remove non-informative kernels. The good thing is that it also performs the kernels parametrization without any need for cross-validation.

Once I've cleaned the paper of all remaining typos and corrections as suggested by the reviewers, I'll put the code online.

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