Efficient manifold preserving edit propagation with adaptive neighborhood size

• We propose an efficient solution for manifold preserving edit propagation.
• We adaptively determine neighbor size for each pixel accordingly to its local complexity in the feature space, instead of using a fixed number of neighbors.
• We combine the hierarchical clustering structure into manifold preserving edit propagation and gain high performance improvement.

Recent manifold preserving edit propagation (Chen et al., 2012) [1] provides a robust way for propagating sparse user edits to a whole image or video, which preserves the manifold structure formed by all pixels in feature space during edit propagation. However, it consumes a big amount of time and memory especially for large images/videos, limiting its practical usage. In this paper, we propose an efficient manifold preserving edit propagation method. We accelerate the original method from two aspects. First, instead of using a fixed neighborhood size in building the manifold structure, we adaptively determine neighborhood size for each pixel based on its local complexity in feature space, which largely reduces average neighborhood size. Secondly, following Xu et al. (2009) [2], we adaptively cluster all pixels, and solve the edit propagation problem on clusters instead of pixels. Our experiment shows that, compared to the original method (Chen et al., 2012) [1], our method significantly reduce time and memory costs without reducing visual fidelity.

Comparison of edit propagation results using our adaptive method and previous methods [Chen 2012].Figure optionsDownload high-quality image (260 K)Download as PowerPoint slide