LAUSR

Colour palettes are finite sets of representative colours for image colours. Similar to any clustering problem, the palette's optimal number of colours is an ill-posed problem. Existing techniques assume the number of colours is given by the user. In this work, the authors introduce an approach to generate dynamic length colour palettes. The authors' approach is based on two-stage clustering. At the first stage, they use k-means-based clustering in order to reduce the number of data points in the given image. Then, the second stage is applied using a density-based clustering. The qualitative results show the effectiveness of the proposed approach. At the end of this Letter, they suggest the best range of colours per palette for natural images based on a study performed on 118,060 images from Microsoft COCO dataset.