  Multiple instance learning (MIL) has attracted great attention recently in
machine learning community. However, most MIL algorithms are very slow and
cannot be applied to large datasets. In this paper, we propose a greedy
strategy to speed up the multiple instance learning process. Our contribution
is two fold. First, we propose a density ratio model, and show that maximizing
a density ratio function is the low bound of the DD model under certain
conditions. Secondly, we make use of a histogram ratio between positive bags
and negative bags to represent the density ratio function and find codebooks
separately for positive bags and negative bags by a greedy strategy. For
testing, we make use of a nearest neighbor strategy to classify new bags. We
test our method on both small benchmark datasets and the large TRECVID MED11
dataset. The experimental results show that our method yields comparable
accuracy to the current state of the art, while being up to at least one order
of magnitude faster.
