Building look & feel concept models from color combinations

Abstract

In this paper, we tackle the problem of associating combinations of colors to abstract concepts (e.g. capricious, classic, cool, delicate, etc.). Since such concepts are difficult to represent using single colors, we consider combinations of colors or color palettes. We leverage two novel databases for color palettes, and learn categorization models using both low and high level descriptors. It is shown that the Bag of Colors and Fisher Vectors are the most rewarding descriptors for palettes categorization and retrieval. A simple but novel and efficient method for cleaning weakly annotated data, whilst preserving the visual coherence of categories is also given. Finally, we demonstrate that abstract category models learned on color palettes can be used in different applications such as image personalization, concept-based palette, and image retrieval and color transfer.