What is it about?
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We introduce a new
problem object co-detection.
Given a set of images with objects observed from two or multiple
images, the goal of co-detection is to detect the objects, establish
the identity of individual object instance, as well as estimate the
viewpoint transformation of corresponding object instances. In
designing
a co-detector, we follow the intuition that an object has consistent
appearance when observed from the same or different viewpoints. By modeling
an object using state-of-the-art part-based representations, we
measure appearance consistency between objects by comparing
part appearance and geometry across images. This allows to effectively
account for object self-occlusions and viewpoint transformations.
Extensive experimental evaluation indicates that our co-detector
obtains more
accurate detection results than if objects were to be detected from
each
image individually. Moreover, we demonstrate the relevance of our
co-detection scheme to other recognition problems such as single
instance
object recognition, wide-baseline matching, and image query.
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(a) Single image object
detection. Notice miss positives and false alarms.
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(b) Object co-detection.
Different colors correspond to different matching objects. Co-detection
recovers missed positives and removes false alarms, compared to single
image object detection.
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Update
May 2013
- We posted the image lists for our experimental evaluation.
We would like to hear your result!
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