One of the most common tasks in image and video editing is the local adjustment of various properties (e.g., saturation or brightness) of regions within an image or video. Edge-aware interpolation of user-drawn scribbles offers a less effort-intensive approach to this problem than traditional region selection and matting. However, the technique suffers a number of limitations, such as reduced performance in the presence of texture contrast, and the inability to handle fragmented appearances. We significantly improve the performance of edge-aware interpolation for this problem by adding a boosting-based classification step that learns to discriminate between the appearance of scribbled pixels. We show that this novel data term in combination with an existing edge-aware optimization technique achieves substantially better results for the local image and video adjustment problem than edge-aware interpolation techniques without classification, or related methods such as matting techniques or graph cut segmentation.