LayerBuilder: Layer Decomposition for Interactive Image and Video Color Editing

Exploring and editing colors in images is a common task in graphic design and photography. However, allowing for interactive recoloring while preserving smooth color blends in the image remains a challenging problem. We present LayerBuilder, an algorithm that decomposes an image or video into a linear combination of colored layers to facilitate color-editing applications. These layers provide an interactive and intuitive means for manipulating individual colors. Our approach reduces color layer extraction to a fast iterative linear system. Layer Builder uses locally linear embedding, which represents pixels as linear combinations of their neighbors, to reduce the number of variables in the linear solve and extract layers that can better preserve color blending effects. We demonstrate our algorithm on recoloring a variety of images and videos, and show its overall effectiveness in recoloring quality and time complexity compared to previous approaches. We also show how this representation can benefit other applications, such as automatic recoloring suggestion, texture synthesis, and color-based filtering.

[1]  Erik Reinhard,et al.  Color Transfer between Images , 2001, IEEE Computer Graphics and Applications.

[2]  Dani Lischinski,et al.  Colorization using optimization , 2004, ACM Trans. Graph..

[3]  David Salesin,et al.  A Bayesian approach to digital matting , 2001, Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001.

[4]  Sylvain Lefebvre,et al.  Appearance-space texture synthesis , 2006, ACM Trans. Graph..

[5]  Jason Lawrence,et al.  Material matting , 2011, ACM Trans. Graph..

[6]  Pat Hanrahan,et al.  Modeling how people extract color themes from images , 2013, CHI.

[7]  Fabio Pellacini,et al.  AppProp: all-pairs appearance-space edit propagation , 2008, ACM Trans. Graph..

[8]  Shi-Min Hu,et al.  Instant Propagation of Sparse Edits on Images and Videos , 2010, Comput. Graph. Forum.

[9]  Pat Hanrahan,et al.  Probabilistic color-by-numbers , 2013, ACM Trans. Graph..

[10]  Dani Lischinski,et al.  A Closed-Form Solution to Natural Image Matting , 2008 .

[11]  Chi-Keung Tang,et al.  KNN Matting , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[12]  Rolf Adams,et al.  Seeded Region Growing , 1994, IEEE Trans. Pattern Anal. Mach. Intell..

[13]  Maneesh Agrawala,et al.  Illumination decomposition for material recoloring with consistent interreflections , 2011, ACM Trans. Graph..

[14]  Edward H. Adelson,et al.  Eurographics Symposium on Rendering 2008 Scribbleboost: Adding Classification to Edge-aware Interpolation of Local Image and Video Adjustments , 2022 .

[15]  Zeev Farbman,et al.  Diffusion maps for edge-aware image editing , 2010, ACM Trans. Graph..

[16]  Chi-Keung Tang,et al.  Local color transfer via probabilistic segmentation by expectation-maximization , 2005, 2005 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR'05).

[17]  Qinping Zhao,et al.  Sparse Dictionary Learning for Edit Propagation of High-Resolution Images , 2014, 2014 IEEE Conference on Computer Vision and Pattern Recognition.

[18]  Xiaowu Chen,et al.  Manifold preserving edit propagation , 2012, ACM Trans. Graph..

[19]  Stephen DiVerdi,et al.  Palette-based photo recoloring , 2015, ACM Trans. Graph..

[20]  S T Roweis,et al.  Nonlinear dimensionality reduction by locally linear embedding. , 2000, Science.

[21]  Shi-Min Hu,et al.  Efficient affinity-based edit propagation using K-D tree , 2009, SIGGRAPH 2009.

[22]  René Vidal,et al.  Estimation of Alpha Mattes for Multiple Image Layers , 2011, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[23]  Sylvain Paris,et al.  User-assisted intrinsic images , 2009, ACM Trans. Graph..