Feature space clustering is a popular approach to image segmentation, in which
a feature vector of local properties (such as intensity, texture or motion) is
computed at each pixel. The feature space is then clustered, and each pixel
is labeled with the cluster that contains its feature vector. A major
limitation of this approach is that feature space clusters generally lack
spatial coherence (i.e., they do not correspond to a compact grouping of
pixels). In this paper, we propose a segmentation algorithm that operates
simultaneously in feature space and in image space. We define an energy
function over both a set of clusters and a labeling of pixels with clusters.
In our framework, a pixel is labeled with a single cluster (rather than, for
example, a distribution over clusters). Our energy function penalizes
clusters that are a poor fit to the data in feature space, and also penalizes
clusters whose pixels lack spatial coherence. The energy function can be
efficiently minimized using graph cuts. Our algorithm can incorporate both
parametric and non-parametric clustering methods. It can be applied to many
optimization-based clustering methods, including k-means and k-medians, and
can handle models which are very close in feature space.
Preliminary results are presented on segmenting real and synthetic images,
using both parametric and non-parametric clustering.