|What is it about?
|| 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
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
accurate detection results than if objects were to be detected from
image individually. Moreover, we demonstrate the relevance of our
co-detection scheme to other recognition problems such as single
object recognition, wide-baseline matching, and image query.
|| We evaluated our algorithm
using image pairs extracted from the following datasets.