Previous formulations of the global Mean Shift clustering algorithm incorporate a global mode finding which requires a lot of computations making it extremely time-consuming. This paper focuses on reducing the computational cost in order to process large document images. We introduce thus a local-global Mean Shift based color image segmentation approach. It is a two-steps procedure carried out by updating and propagating cluster parameters using the mode seeking property of the global Mean Shift procedure. The first step consists in shifting each pixel in the image according to its R-Nearest Neighbor Colors(R-NCC) in the spatial domain. The second step process shifts only the previously extracted local modes according to the entire pixels of the image. Our proposition has mainly three properties compared to the global Mean Shift clustering algorithm: 1) an adaptive strategy with the introduction of local constraints in each shifting process, 2) a combined feature space of both the color and the spatial information, 3) a lower computational cost by reducing the complexity. Assuming all these properties, our approach can be used for fast pre-processing of real old document images. Experimental results show its desired ability for image restoration; mainly for ink bleed-through removal, specific document image degradation.