We propose an image segmentation method that divides an image into foreground and
background regions when the approximate color distributions for these regions are given.
Our approach was inspired by global consistency measures that directly evaluate the similarity
between a given distribution and the distribution of the resulting segmentation,
which were recently proposed in order to overcome the limitations of traditional pixelwise
(local) consistency measures. The main feature of our proposal is that it uses two
(foreground and background) input distributions, which increases the robustness compared
to previous studies. To achieve this, we formulated a new mathematical model that
describes the consistencies between the two input distributions and the segmentation, in
which weighting parameters for the two distribution matching terms are set to be approximately
proportional to the size of the foreground and background areas. We call this dual
distribution matching (DDM). We also derived an optimization method that uses graph
cuts. Experimental results that show the effectiveness of our method and comparisons
between local and global consistency measures are presented.