In this paper, a region based parametric motion estimation approach is proposed. As a feature based approach, sequential images are firstly represented in a multi-scale region hierarchy, then temporally adjacent region sets are matched along scale-space by symbolic optimization of compound criteria based region similarity, so that temporal region correspondence and the parametric motion representation can be obtained. The motion of regions is represented in affine transformation and estimated in a coarse-to-fine manner so that an intrinsic topological constraint can be applied to enhance the stability of the optimization. Finally, the regions are grouped to uniform motion region (UMR) by their motion consistency to realize a compact representation. Compared with classical optical flow methods, our approach shows three advantages. First, motion meaning is easily to interpret in high-level. Second, fundamental problem such as aperture problem, boundary problem, or large motion problem can be some what tackled by applying structure information. Third, computation is more stable and efficient because of the symbolic feature-based methodology and the multi-scale framework.