Image partitioning separates an image into multiple visually
and semantically homogeneous regions, providing a summary of visual
content. Knowing that human observers focus on interesting objects
or regions when interpreting a scene, and envisioning the usefulness of
this focus in many computer vision tasks, this paper develops a userattention
adaptive image partitioning approach. Given a set of pairs of
oversegments labeled by a user as “should be merged” or “should not
be merged”, the proposed approach produces a fine partitioning in user
defined interesting areas, to retain interesting information, and a coarser
partitioning in other regions to provide a parsimonious representation.
To achieve this, a novel Markov Random Field (MRF) model is used to
optimally infer the relationship (“merge” or “not merge”) among oversegment
pairs, by using the graph nodes to describe the relationship
between pairs. By training an SVM classifier to provide the data term,
a graph-cut algorithm is employed to infer the best MRF configuration.
We discuss the difficulty in translating this configuration back to an
image labelling, and develop a non-trivial post-processing to refine the
configuration further. Experimental verification on benchmark data sets
demonstrates the effectiveness of the proposed approach.