In this paper, we present a new voting-based object labeling
method that is robust to background clutter. The
conventional simple voting method shows very poor performance
under clutter. To reduce the effect of clutter, first we
aggregate the weights between the features and the support
features using similarity and proximity. Through the recursive
weight aggregation process, features belonging to the
same objects get stronger weights, and features belonging
to clutter get weaker weights. Then, we vote the weightaggregated
features to get the object labels. We validate
the enhancement of the proposed method by using an open
database and a real test set.