Talks and Poster Presentations (with Proceedings-Entry):

A. Ion, J. Carreira, C. Sminchisescu:
"Image Segmentation by Figure-Ground Composition into Maximal Cliques";
Talk: 13th IEEE International Conference on Computer Vision, Barcelona, Spanien; 2011-11-06 - 2011-11-13; in: "13th IEEE International Conference on Computer Vision", IEEE, (2011), ISBN: 978-1-4577-1100-8; 2110 - 2117.

English abstract:
We propose a mid-level statistical model for image segmentation
that composes multiple figure-ground hypotheses
(FG) obtained by applying constraints at different locations
and scales, into larger interpretations (tilings) of
the entire image. Inference is cast as optimization over
sets of maximal cliques sampled from a graph connecting
all non-overlapping figure-ground segment hypotheses. Potential
functions over cliques combine unary, Gestalt-based
figure qualities, and pairwise compatibilities among spatially
neighboring segments, constrained by T-junctions and
the boundary interface statistics of real scenes. Learning
the model parameters is based on maximum likelihood, alternating
between sampling image tilings and optimizing
their potential function parameters. State of the art results
are reported on the Berkeley and Stanford segmentation
datasets, as well as VOC2009, where a 28% improvement
was achieved.

Electronic version of the publication:

Created from the Publication Database of the Vienna University of Technology.