Fast Robust Large-scale Mapping from Video and Internet Photo ...

Figure 3: A large gist cluster for the Statue of Liberty dataset. We show the hundred
images closest to the cluster mean. The effectiveness of the low-dimensional gist representation
can be gauged by noting that even without enforcing geometric consistency, these
clusters display a remarkable degree of structural similarity.
is sensitive to image variation such as clutter as for example people in front
of the camera, lighting conditions, and camera zoom. We found that the
large clusters are typically almost outlier free (see Figure 3 for an example).
The smaller clusters experiences significantly more noise or belong to the
unrelated images in the photo collection. The clusters now define a loose
ordering of the dataset that defines corresponding images as being in the
same cluster. The next step will employ this order to efficiently identify
overlapping images as well as the outlier images on a per cluster basis using
the same techniques (Section 4.1.2) as in the case of video streams.
4.4.2. Iconic Image Registration
While the clusters define a local relationship our method still needs to
establish a global relationship between the photos. To facilitate an efficient
global registration we further summarize the dataset. The objective of the
next step is to find a representative image (iconic) for each cluster of views.
This iconic is then later used for efficient global registration. The second
objective is to enforce a valid two-view geometry with respect to the iconic
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