  In this paper we attempt to address the problem of geometric multi-model
fitting with resorting to a few weakly annotated (WA) data points, which has
been sparsely studied so far. In weak annotating, most of the manual
annotations are supposed to be correct yet inevitably mixed with incorrect
ones. The WA data can be naturally obtained in an interactive way for specific
tasks, for example, in the case of homography estimation, one can easily
annotate points on the same plane/object with a single label by observing the
image. Motivated by this, we propose a novel method to make full use of the WA
data to boost the multi-model fitting performance. Specifically, a graph for
model proposal sampling is first constructed using the WA data, given the prior
that the WA data annotated with the same weak label has a high probability of
being assigned to the same model. By incorporating this prior knowledge into
the calculation of edge probabilities, vertices (i.e., data points) lie on/near
the latent model are likely to connect together and further form a
subset/cluster for effective proposals generation. With the proposals
generated, the $\alpha$-expansion is adopted for labeling, and our method in
return updates the proposals. This works in an iterative way. Extensive
experiments validate our method and show that the proposed method produces
noticeably better results than state-of-the-art techniques in most cases.
