Automatic Palette Extraction for Image Editing

Interactive palette based colour editing applications have grown in popularity in recent years, but while
many methods propose fast palette extraction techniques, they typically rely on the user to define the number
of colours needed. In this paper, we present an approach that extracts a small set of representative colours
from an image automatically, determining the optimal palette size without user interaction. Our iterative
technique assigns a vote to each pixel in the image based on how close they are in colour space to the colours
already in the palette. We use a histogram to divide the colours into bins and determine which colour occurs
most frequently in the image but is far away from all of the palette colours, and we add this colour to the
palette. This process continues until all pixels in the image are well represented by the palette. Comparisons
with existing methods show that our colour palettes compare well to other state of the art techniques, while
also computing the optimal number of colours automatically at interactive speeds. In addition, we showcase
how our colour palette performs when used in image editing applications such as colour transfer and layer
decomposition.

Paper

Automatic Palette Extraction for Image Editing

Collaborators

Mairéad Grogan, Matis Hudon, Daniel McCormack, Aljosa Smolic.

Implementation

Code coming soom.

Reference

Mairéad Grogan, Matis Hudon, Daniel McCormack and Aljosa Smolic.
Automatic Palette Extraction for Image Editing.
Irish Machine Vision and Image Processing Conference, Belfast, 2018.

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