Automatic color palette

We present a method for the automatic estimation of the minimum set of colors needed to describe an image. We call this minimal set “color palette”. The proposed method combines the well-known K-Means clustering technique with a thorough analysis of the color information of the image. The initial set of cluster seeds used in K-Means is automatically inferred from this analysis. Color information is analyzed by studying the 1D histograms associated to the hue, saturation and intensity components of the image colors. In order to achieve a proper parsing of these 1D histograms a new histogram segmentation technique is proposed. The experimental results seem to endorse the capacity of the method to obtain the most significant colors in the image, even if they belong to small details in the scene. The obtained palette can be combined with a dictionary of color names in order to provide a qualitative image description.