monday 10 june 2013
- new algorithms: SDCA (Shalev-Shwartz 2013), SAG (Le Roux 2012)
- new custom matrix kernel to handle train and test separately
- add fvec file format
- add experimental package for linear algebra and corresponding processing (i.e. PCA, KPCA), use at your own risk!
- add example app to perform VOC style classification
- Lots of bug fixes
The linear algebra package is at the moment very rough. I find it somehow useful to perform some king of pre-processing (like a PCA for example). At the moment, my matrix code is a bit slow. If ever I find the time to make solid matrix operations, I will add some nice features like low rank approximations of kernels (Nyström).
Nevertheless, I suggest to always pick the latest git version instead of these releases. The API is very stable now and should not change significantly, which means that all the code you write now is to be supported in the next few years. Thus, picking the latest git always assures you to have the bug-fixes and so on (I don't release versions only for bug-fixes).
One more thing: JKernelMachines has been published in JMLR last month. I encourage you to read the paper and to cite it if you ever use to code for your publications.
wednesday 10 april 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.
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.
tuesday 05 march 2013
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
friday 16 november 2012
I am organizing a special session with Philippe Gosselin at this year's ESANN conference.
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.
Submission of full paper: November 30, 2012
Notification of acceptance: February 1, 2013
monday 17 september 2012
My fellow colleague Aymeric Histace is organizing a workshop at VISIGRAPP following our work on the GeoDiff projet. The key topic is the study of complex diffusion processes by variational approaches and multi-agent systems. I'll be part of the committee to give some help. This workshop is totally open to contributions, so I hope we will have many interesting papers to review. We already have programmed some nice tutorials on MAS.
Here is the scope:
The main aim of this special session is to bring together researchers who are interested in variational methods and by multi agent system in order to (i) reflect on a state-of- the-art on their abilities to model reaction--diffusion processes, and (ii) to discuss potential new applications since these two different types of tools are not generally 3 associated with each other whereas such a joint applications can be of primary interest to model complex diffusion process at different scales of the phenomenon.
A second objective of this special session, but not necessarily less important than the first one, is to imagine through this particular application context new tools that will find future applicability in image processing (image restoration or inpainting mainly).
And the important dates:
Paper Submission: November 8, 2012
Authors Notification: December 6, 2012
Final Paper Submission and Registration: December 18, 2012