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Data-Driven Image Color Theme Enhancement

Student Presentation. Data-Driven Image Color Theme Enhancement. Sou -Young Jin Dept. of Computer Science, KAIST souyoungjin@gmail.com.

december
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Data-Driven Image Color Theme Enhancement

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  1. Student Presentation Data-Driven Image Color Theme Enhancement Sou-Young Jin Dept. of Computer Science, KAIST souyoungjin@gmail.com Baoyuan Wang, Yizhou Yu, Tien-Tsin Wong, Chun Chen, Ying-Qing Xu, “Data-Driven Image Color Theme Enhancement,” ACM Transactions on Graphics (ToG), 2010 EE838B - Advanced Topics in Image Processing

  2. Image Color Theme Enhancement color theme Original image recolored result Given a color theme, to transform the colors in the original image close to a desired color theme

  3. Existing Approaches (1/2) Adobe Photoshop: difficult to edit image theme colors

  4. Existing Approaches (2/2) Input image Input image Result Unnatural results that violate common knowledge Result Reference image • Histogram matching • Map the original colors to a new range of colors in a global manner • Color transferring with a reference image [Pitieet al. 2007] • Highly rely on the underlying color statistics consistence

  5. Goal of this Paper • Image color theme enhancement • New color composition is close to a desired color theme • Maintaining the realism of natural images • To learn the relationships between texture classes and color histograms • Natural materials are highly related to textures • Learn the likelihoods of a certain texture having a certain color • Consider this relationship as an important factor for color editing  Proposed framework is composed of • Offline phase: to extract prior knowledge (texture-color relationship) • Online phase: to edit colors of an input image

  6. Prior knowledge extraction (Offline) (1/2) Image Database Theme Database Labeling as “Firestone” Computing distance between the given color themes Quantizing colors in LAB space using the K-means algorithm Color theme based image labeling

  7. Prior knowledge extraction (Offline) (2/2) Firestone Texture #1 (leaf) … … Texture#200 (sky) … Morning Texture #1 (leaf) … … Texture#200 (sky) … Texture-Color Relationship

  8. Soft Segmentation (runtime) Texture #1 Texture #30 Texture #77 Texture #46 Soft segments C C C C C C 1 2 3 4 i i : probability of pixel t belonging to segment i Final color of a pixel is

  9. Color Optimization (Runtime) (1/2) • To recolor each soft segment • To balance between three constraints • E1: Color constraints • To keep the original image as much as possible • Scribble colors or original colors • E2: (Penalty function) Texture-color relationships • To maintain naturalness and realism • To check if the texture of each pixel is admissible with the new color • E3: Target color theme • To steer towards the desired color theme • Compute the distance in color mood space (activity, weight, heat) • This energy function is minimized using sequential quadratic programing

  10. Color Optimization (Runtime) (2/2) E1 : Distance between colors in the original image and colors in the new image Original image Texture #21 Color = (0, 0) Texture #21’s histogram E2 : Negative of the probability value for the corresponding texture’s histogram New recolored image Texture-color relationship histogram E3: Distance between colors in the original image and colors in the desired color theme Color theme

  11. Results (1/2) Original image

  12. Results (2/2) Original image

  13. Overview of the Proposed Approach • Three main contributions • Texture-color relationship for more natural color enhancement • Color enhancement problem as an optimization problem • Quantification of differences between an image and color theme

  14. Limitation on Textureless Regions Original image Recolored image • Prior knowledge cannot provide enough constraints

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