Multiple Instance Learning for Automatic Image Annotation

Most traditional approaches for automatic image annotation cannot provide reliable annotations at the object level because it could be very expensive to obtain large amounts of labeled object-level images associated to individual regions. To reduce the cost for manually annotating at the object level, multiple instance learning, which can leverage loosely-labeled training images for object classifier training, has become a popular research topic in the multimedia research community. One bottleneck for supporting multiple instance learning is the computational cost on searching and identifying positive instances in the positive bags. In this paper, a novel two-stage multiple instance learning algorithm is developed for automatic image annotation. The affinity propagation(AP) clustering technique is performed on the instances both in the positive bags and the negative bags to identify the candidates of the positive instances and initialize the maximum searching of Diverse Density likelihood in the first stage. In the second stage, the most positive instances are then selected out in each bag to simply the computing procedure of Diverse Density likelihood. Our experiments on two well-known image sets have provided very positive results.