The Role
of Image Understanding in Contour Detection
Larry Zitnick
and Devi
Parikh
Abstract
Many cues
have been proposed for contour detection or image segmentation. These
include low-level image gradients to high-level information such as the
identity of the objects in the scene or 3D depth understanding. While
stateof-the-art approaches have been incorporating more cues, the
relative importance of the cues is unclear. In this paper, we examine
the relative importance of low-, mid- and high-level cues to gain a
better understanding of their role in detecting object contours in an
image. To accomplish this task, we conduct numerous human studies and
compare their performance to several popular segmentation and contour
detection machine approaches. Our findings suggest that the current
state-of-the-art contour detection algorithms perform as well as humans
using low-level cues. We also find evidence that the recognition of
objects, but not occlusion information, leads to improved human
performance. Moreover, when objects are recognized by humans, their
contour detection performance increases over current machine
algorithms. Finally, mid-level cues appear to offer a larger
performance boost than high-level cues such as recognition.
Patches Dataset
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Publications
C. L Zitnick and D. Parikh
The Role of Image Understanding in Contour Detection
IEEE Conference on Computer Vision and Pattern Reocgnition (CVPR), 2012
[poster]
[Thanks
to Yong
Jae Lee
for the webpage template]