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.