Computational framework for the white point interpretation based on nameability

In this work we present a framework for white point estimation of images under uncalibrated conditions where multiple interpretable solutions can be considered. In this way, we propose to use the colour matching visual cue that has been proved as related to colour constancy. The colour matching process is guided by the introduction of semantic information regarding the image content. Thus, we introduce high-level information of colours we expect to find in the images. Considering these two ideas, colour matching and semantic information, and existing computational colour constancy approaches, we propose a white point estimation method for uncalibrated conditions which delivers multiple solutions according to different interpretations of the colours in a scene. However, we present the selection of multiple solutions which enables to obtain more information of the scene than existing colour constancy methods, which normally select a unique solution. In this case, the multiple solutions are weighted by the degree of colour matching between colours in the image and semantic information introduced. Finally, we prove that the feasible set of solutions can be reduced to a smaller and more significant set with a semantic interpretation. Our study is framed in a global image annotation project which aims to obtain descriptors which depict the image, in this work we focus on illuminant descriptors. We define two different sets of conditions for this project: (a) calibrated conditions, when we have some information about the acquisition process and (b) uncalibrated conditions, when we do not know the acquisition process. Although we have focused on the uncalibrated case, for calibrated conditions we also propose a colour constancy method which introduces the relaxed grey-world assumption to produce a reduced feasible set of solutions. This method delivers good performance similar to existing methods and reduces the size of the feasible set obtained.