Colorization using optimization
ACM SIGGRAPH 2004 Papers
Interactive local adjustment of tonal values
ACM SIGGRAPH 2006 Papers
AppWand: editing measured materials using appearance-driven optimization
ACM SIGGRAPH 2007 papers
Efficient gradient-domain compositing using quadtrees
ACM SIGGRAPH 2007 papers
AppProp: all-pairs appearance-space edit propagation
ACM SIGGRAPH 2008 papers
IEEE Transactions on Pattern Analysis and Machine Intelligence
Gaussian KD-trees for fast high-dimensional filtering
ACM SIGGRAPH 2009 papers
Efficient affinity-based edit propagation using K-D tree
ACM SIGGRAPH Asia 2009 papers
Diffusion maps for edge-aware image editing
ACM SIGGRAPH Asia 2010 papers
Efficient Edit Propagation Using Hierarchical Data Structure
IEEE Transactions on Visualization and Computer Graphics
Hierarchical Event Selection for Video Storyboards with a Case Study on Snooker Video Visualization
IEEE Transactions on Visualization and Computer Graphics
Video matting via opacity propagation
The Visual Computer: International Journal of Computer Graphics
Fast hierarchical animated object decomposition using approximately invariant signature
The Visual Computer: International Journal of Computer Graphics
Hierarchical Streamline Bundles
IEEE Transactions on Visualization and Computer Graphics
Manifold preserving edit propagation
ACM Transactions on Graphics (TOG) - Proceedings of ACM SIGGRAPH Asia 2012
VEA 2012: Efficient antialiased edit propagation for images and videos
Computers and Graphics
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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.