Towards automatic bounding box annotations from weakly labeled images

In this work we discuss the problem of automatically determining bounding box annotations for objects in images whereas we only assume weak labeling in the form of global image labels. We therefore are only given a set of positive images all containing at least one instance of a desired object and a negative set of images which represent background. Our goal is then to determine the locations of the object instances within the positive images by bounding boxes. We also describe and analyze a method for automatic bounding box annotation which consists of two major steps. First, we apply a statistical model for determining visual features which are likely to be indicative for the respective object class. Based on these feature models we infer preliminary estimations for bounding boxes. Second, we use a CCCP training algorithm for latent structured SVM in order to improve the initial estimations by using them as initializations for latent variables modeling the optimal bounding box positions. We evaluate our approach on three publicly available datasets.