Mining partially annotated images

In this paper, we study the problem of mining partially annotated images. We first define what the problem of mining partially annotated images is, and argue that in many real-world applications annotated images are typically partially annotated and thus that the problem of mining partially annotated images exists in many situations. We then propose an effective solution to this problem based on a statistical model we have developed called the Semi-Supervised Correspondence Hierarchical Dirichlet Process (SSCHDP). The main idea of this model lies in exploiting the information pertaining to partially annotated images or even unannotated images to achieve semi-supervised learning under the HDP structure. We apply this model to completing the annotations appropriately for partially annotated images in the training data and then to predicting the annotations appropriately and completely for all the unannotated images either in the training data or in any unseen data beyond the training process. Experiments show that SSC-HDP is superior to the peer models from the recent literature when they are applied to solving the problem of mining partially annotated images.