Data driven image color theme enhancement
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Student Presentation. Data-Driven Image Color Theme Enhancement. Sou -Young Jin Dept. of Computer Science, KAIST [email protected]

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Data-Driven Image Color Theme Enhancement

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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

[email protected]

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


Image color theme enhancement

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


Existing approaches 1 2

Existing Approaches (1/2)

Adobe Photoshop: difficult to edit image theme colors


Existing approaches 2 2

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


Goal of t his paper

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


Prior knowledge extraction offline 1 2

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


Prior knowledge extraction offline 2 2

Prior knowledge extraction (Offline) (2/2)

Firestone

Texture #1

(leaf)

Texture#200

(sky)

Morning

Texture #1

(leaf)

Texture#200

(sky)

Texture-Color Relationship


Soft segmentation runtime

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


Color optimization runtime 1 2

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


Color optimization runtime 2 2

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


Results 1 2

Results (1/2)

Original image


Results 2 2

Results (2/2)

Original image


Overview of the proposed approach

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


Limitation on t extureless regions

Limitation on Textureless Regions

Original image

Recolored image

  • Prior knowledge cannot provide enough constraints


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