No news, good news

This post should explain why I did not set anything new here for long time. First, I've been very busy doing research the past year. In no particular order:

  • My very first PhD student - Romain Negrel - has successfully defended in December 2014. He is now pursuing a postdoc at GREY, Caen, France.
  • I've been invited for a month at the TU Darmstadt between August and September, working with Dr.-Ing. V. Willert and Thomas Guthier on learning local descriptors. While the work isn't finished yet (read not published), it is very interesting. All of this was funded by the DAAD, which I thank gratefully.
  • I've been working a lot recently on computer vision and image processing for cultural heritage purposes. To that end, I've set up a benchmark using images from the BnF, based on a project we have funded by the Labex Patrima. A paper on that has been accepted in IEEE Signal Processing Magazine. With respect to the project, we are starting to work on interesting things involving deep learning with my new postdoc Yi Ren.
  • More on cultural heritage and image processing, I'm organizing a special session at this year's edition of the GRETSI. The call for paper is here, you are of course all invited to submit as many paper as you can to share new work in this interesting area.
  • I've been publishing many things in the past months (at my scale of course), all of which you can find in the publications page.

Now for the things scarcely related to research, I've been elected head of the computer science department at the ENSEA, which means that I now have a lot of administrative things to do. If you have any inquiries regarding CS at our graduate school, I guess I am now the guy to ask.

Also, I've been releasing an album with my oldest band, which you can download for free here.

Some Publications, JKMS, ESANN

2014 is set to be a good year! We already have the reviews for a few papers I've been working on lately. Some are in the ML domain (an ICPR paper with Romain Negrel on supervised sparse subspace learning, an ESANN paper with Jérôme Fellus on decentralized PCA), others in CV (2 journals in revision on low level visual descriptors with Olivier Kihl) and 1 in 3D indexing with Hedi Tabia (CVPR poster)

Other than that, I've been pushing version 2.3 of jkms. I've tagged it the "density edition" since most of the changes are related to density estimators (mostly one class SVM). I've introduced the density version of SimpleMKL, which could e useful to perform model selection. Basically, if you set C=1, you'll get a Parzen estimator, albeit selection the kernel from a specific set.

Finally, I'll be in Brugge next week for the ESANN 2014 conference. A good way to start new projects, if anyone volunteers!

BMVC 2013, 3DOR2014

I'll be at BMVC in Bristol next week to present some work done with Hedi Tabia on 3D similarities using curves.

Similarity on curves is usually very expansive since it requires to re-parametrize one of the curves so as to map it to the same affine space as the other. To circumvent the heavy processing, we proposed to use the Nyström approximation for kernels on some well chosen training set found by active learning. Nothing excessively fancy, but very very effective in computational time.

On the other hand, Hedi is co-organizing the next 3DOR 2014, which will be held in Strasbourg just before Eurographics. The website is brand new, so expect more information soon.

JKernelMachines 2.0

JKernelMachines version number bumped to 2.0!

The bigs changes are:

  • All classes have migrated under fr.lip6.jkernelmachines.* This breaks backward compatibility! (hence the 2.0 version number).
  • Separation of the core library and unit testing
  • Junit testing added
  • Lots of bug fixes
  • Better examples, and many useless test classes removed
  • A small demo script to benchmark the library was added
As always, check the mloss page or directly github.

ESANN 2012 Special Session on Multimedia

I am organizing a special session with Philippe Gosselin at this year's ESANN conference.

Machine Learning for multimedia applications
David Picard , ETIS – ENSEA, Philippe-Henri Gosselin , INRIA Rennes (France)

In recent years, many multimedia applications have shown very successful improvements by leveraging machine learning techniques. These applications include image and video classification, object recognition, image and video retrieval, or event detection.

However, these multimedia applications also uncover new machine learning problems in areas such as mid-level features learning, distance learning, feature combination, and so on.

This special session is intended to research papers that combine machine learning for multimedia problems. The following topics are of particular interest:

  • Mid-level features learning, Deep learning
  • Feature combination, Multiple kernel learning
  • Kernel methods, Kernel learning, Distance learning
  • Machine learning methods specially adapted to image classification, image and video retrieval, object or event recognition, etc.
Important Dates:
Submission of full paper: November 30, 2012
Notification of acceptance: February 1, 2013

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