Kernel methods are a new theoretical and algorithmic framework for machine learning. By representing data through well defined dot-products, referred to as kernels, they allow to use classical linear supervised machine learning algorithms to non linear settings and to non vectorial data. A major issue when applying these methods to image processing or computer vision is the choice of the kernel. I will present recent advances in the design of kernels for images that take into account the natural structure of images.