Аннотация:Binary energy optimization is a popular approach for
segmenting an image into foreground/background regions.
To model region appearance, color, a relatively high dimensional
feature, should be handled effectively. A full
color histogram is usually too sparse to be reliable. One
approach is to reduce dimensionality by color space clustering.
Another popular approach is to fit GMMs for soft
color space clustering. These approaches work well when
the foreground/background are sufficiently distinct. In cases
of more subtle difference in appearance, both approaches
may reduce or even eliminate foreground/background distinction.
This happens because either color clustering is
performed completely independently from segmentation, as
a preprocessing step (in clustering), or independently for
the foreground and independently for the background (in
GMM). We propose to make clustering an integral part
of segmentation, by including a new clustering term in
the energy. Our energy favors clusterings that make foreground/background
appearance more distinct. Exact optimization
is not feasible, therefore we develop an approximate
algorithm. We show the advantage of including the
color clustering term into the energy function on camou-
flage images, as well as standard segmentation datasets.