  We propose a new approach to interactive image segmentation based on some
properties of a family of quadratic optimization problems related to dominant
sets, a well-known graph-theoretic notion of a cluster which generalizes the
concept of a maximal clique to edge-weighted graphs. In particular, we show
that by properly controlling a regularization parameter which determines the
structure and the scale of the underlying problem, we are in a position to
extract groups of dominant-set clusters which are constrained to contain
user-selected elements. The resulting algorithm can deal naturally with any
type of input modality, including scribbles, sloppy contours, and bounding
boxes, and is able to robustly handle noisy annotations on the part of the
user. Experiments on standard benchmark datasets show the effectiveness of our
approach as compared to state-of-the-art algorithms on a variety of natural
images under several input conditions.
