  In this paper, we present a new multiple instance learning (MIL) method,
called MIS-Boost, which learns discriminative instance prototypes by explicit
instance selection in a boosting framework. Unlike previous instance selection
based MIL methods, we do not restrict the prototypes to a discrete set of
training instances but allow them to take arbitrary values in the instance
feature space. We also do not restrict the total number of prototypes and the
number of selected-instances per bag; these quantities are completely
data-driven. We show that MIS-Boost outperforms state-of-the-art MIL methods on
a number of benchmark datasets. We also apply MIS-Boost to large-scale image
classification, where we show that the automatically selected prototypes map to
visually meaningful image regions.
