Bag-level multi-instance active learning for image retrieval

By extensively studying the characteristics of active learning in multiple-instance setting,the multiple instance active learning problem(MIAL) was categorized into three paradigms,i.e.bag-level active learning,instance-level active learning and mixture-level active learning.Furthermore,a novel sample selection strategy was proposed to tackle the bag-level MIAL problem,in which the statistical feature of instance number,an important factor in MIL setting,was integrated with the sample uncertainty simultaneously.Experiments were conducted on the Corel image dataset and the results show that,compared with several traditional sample selection strategies,the proposed method can effectively reduce the labor of manual annotating and improve the performance of the multi-instance learner.