High-level, or holistic, scene understanding involves
reasoning about objects, regions, and the 3D relationships
between them. This requires a representation above the
level of ...
User-provided object bounding box is a simple and
popular interaction paradigm considered by many existing
interactive image segmentation frameworks. However,
these frameworks t...
Victor Lempitsky, Pushmeet Kohli, Carsten Rother, ...
In this paper, we investigate the detection of semantic
human actions in complex scenes. Unlike conventional
action recognition in well-controlled environments,
action detection...
Object class models trained on hundreds or thousands of
images have shown to enable robust detection. Transferring
knowledge from such models to new object classes trained
from ...
Mean shift clustering is a powerful unsupervised data
analysis technique which does not require prior knowledge
of the number of clusters, and does not constrain the shape
of th...
Prominent feature point descriptors such as SIFT and
SURF allow reliable real-time matching but at a compu-
tational cost that limits the number of points that can be
handled on...
Michael Calonder, Vincent Lepetit, Pascal Fua, Kur...
This paper is focused on the Co-segmentation problem
[1] – where the objective is to segment a similar object from
a pair of images. The background in the two images may be
ar...
Geometric rearrangement of images includes operations
such as image retargeting, inpainting, or object rearrangement.
Each such operation can be characterized by a shiftmap:
the...
Image auto-annotation is an important open problem in
computer vision. For this task we propose TagProp, a discriminatively
trained nearest neighbor model. Tags of test
images a...
Matthieu Guillaumin, Thomas Mensink, Jakob Verbeek...
We consider a class of region-based energies for image
segmentation and inpainting which combine region integrals
with curvature regularity of the region boundary. To
minimize s...