International audienceOur goal is to represent images in terms of geometric objects acting as primitive elements of an image description. Similar representations obtained by stochastic marked point processes have already led to convincing image analysis results but suffer from serious drawbacks such as complex and unstable parameter tuning, large computing time, and lack of generality. We propose an alternative descriptive model based on a Jump-Diffusion process which can be performed in shorter computing times and applied to a variety of applications without changing the model or modifying the tuning parameters. In our approach, a probabilistic Gibbs model is adapted to a library of geometric objects and is sampled by a Jump-Diffusion process in order to closely match an underlying texture. Experiments with natural textures and remotely sensed images show good potentialities of the proposed approach
