Multiple-Instance learning from multiple perspectives: Combining models for Multiple-Instance learning

Multiple-Instance learning (MIL), which relaxes training annotation granularity from instance level to instance collection (bag) level by applying bag concept, obtains increasing attentions from computer vision community. Due to its flexible annotation mechanism, MIL has been naturally utilized on a variety of computer vision problems. And numerous models have been proposed, each of which is ingeniously designed to catch certain characteristics of MIL. However different models only perform well on certain tasks, and further improvement can hardly be achieved. In this paper, we propose a framework that combines multiple complementary models for solving MIL. Multiple-kernel learning as well as boosting based ensemble learning are utilized to achieve optimal combination. Moreover, the framework is extended to integrate active learning, so as to further reduce the annotation costs on acquiring an accurate image classifier. Experimental studies demonstrate the effectiveness of the proposed methods.