Automatic color palette
Julie Delon, Agnès Desolneux, José-Luis Lisani, Ana Belén Petro · Inverse Problems and Imaging · 2007
We present a method for the automatic estimation of the minimum set ofcolors needed to describe an image. We call this minimal set''color palette''.The proposed method combines the well-known K-Means clustering technique witha thorough analysis of the color information of the image.The initial set of cluster seeds used in K-Means is automatically inferred fromthis analysis.Color information is analyzed by studying the 1D histogramsassociated to the hue, saturation and intensity componentsof the image colors. In order to achieve a proper parsing of these1D histograms a new histogram segmentation technique is proposed.The experimentalresults 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 orderto provide a qualitative image description.