Automatic tag-to-region assignment via multiple instance learning

Translating image tags at the image level to regions (i.e., tag-to-region assignment), which could play an important role in leveraging loosely-labeled training images for object classifier training, has become a popular research topic in the multimedia research community. In this paper, a novel two-stage multiple instance learning algorithm is presented for automatic tag-to-region assignment. The regions are generated by performing multiple-scale image segmentation and the instances with unique semantics are selected out from those regions by a random walk process. The affinity propagation (AP) clustering technique and Hausdorff distance are performed on the instances to identify the most positive instance and utilize it to initialize the maximum searching of Diverse Density likelihood in the first stage. In the second stage, the most contributive instance, which is chosen from each bag, is treated as the key instance for simplifying the computing procedure of Diverse Density likelihood. At last, an automatic method is proposed to discriminate the boundary between positive instances and negative instances. Our experiments on three well-known image sets have provided positive results.