ScribbleBoost: Adding Classification to Edge-Aware Interpolation of Local Image and Video Adjustments
Yuanzhen Li 1, Edward H. Adelson 1, Aseem Agarwala 2
Dept. of Brain and Cognitive Sciences, and Computer Science and Artificial Intelligence
2 Adobe Systems, Inc
To Appear in Graphics Forum, Proceedings of Eurographics Symposium on Rendering 2008
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.
Supplemental Material: User Study