Motion-Compensated Frame Interpolation (MCFI) is a technique used extensively for increasing the temporal frequency of a video sequence. In order to obtain a high quality interpolation, the motion field between frames must be well-estimated. However, many current techniques for determining the motion are prone to errors in occlusion regions, as well as regions with repetitive structure. An algorithm is proposed for improving both the objective and subjective quality of MCFI by refining the motion vector field. A Discriminant Saliency classifier is employed to determine regions of the motion field which are most important to a human observer. These regions are refined using a multi-stage motion vector refinement which promotes candidates based on their likelihood given a local neighborhood. For regions which fall below the saliency threshold, frame segmentation is used to locate regions of homogeneous color and texture via Normalized Cuts. Motion vectors are promoted such that each homogeneous region has a consistent motion. Experimental results demonstrate an improvement over previous methods in both objective and subjective picture quality