Multiple-instance Learning based on Bernoulli Mixture Model

Abstract

Multiple-instance learning (MIL) is a form of weakly supervised learning. Its instances are arranged in groups (called bags), and labels are provided for the entire bag after training. The classic MIL method represents examples with pre-calculated features, and the classification process is also cumbersome. In this paper, we use neural networks to extract features, parameterize all transformations, and use Bernoulli mixture model to construct MIL models for baggage tags, using simpler network structures and more accurately solving these problems. Experiments show that our results can be competitive with the classical MIL algorithm on the MINST dataset.