Multiple-Instance Learning with Empirical Estimation Guided Instance Selection

The embedding based framework handles the multiple-instance learning (MIL) via the instance selection and embedding. It is how to select instance prototypes that becomes the main difference between various algorithms. Most current studies depend on single criteria for selecting instance prototypes. In this paper, we adopt two kinds of instance-selection criteria from two different views. For the combination of the two-view criteria, we also present an empirical estimator under which the two criteria compete for the instance selection. Experimental results validate the effectiveness of the proposed empirical estimator based instance-selection method for MIL.