  Intrinsic image decomposition is a severely under-constrained problem. User
interactions can help to reduce the ambiguity of the decomposition
considerably. The traditional way of user interaction is to draw scribbles that
indicate regions with constant reflectance or shading. However the effect
scopes of the scribbles are quite limited, so dozens of scribbles are often
needed to rectify the whole decomposition, which is time consuming. In this
paper we propose an efficient way of user interaction that users need only to
annotate the color composition of the image. Color composition reveals the
global distribution of reflectance, so it can help to adapt the whole
decomposition directly. We build a generative model of the process that the
albedo of the material produces both the reflectance through imaging and the
color labels by color naming. Our model fuses effectively the physical
properties of image formation and the top-down information from human color
perception. Experimental results show that color naming can improve the
performance of intrinsic image decomposition, especially in cleaning the
shadows left in reflectance and solving the color constancy problem.
