Figure-Ground Image Segmentation Helps Weakly-Supervised Learning of Objects

Given a collection of images containing a common object, we seek to learn a model for the object without the use of bounding boxes or segmentation masks. In linguistics, a single document provides no information about location of the topics it contains. On the contrary, an image has a lot to tell us about where foreground and background topics lie. Extensive literature on modelling bottom-up saliency and pop-out aims at predicting eye fixations and allocation of visual attention in a single image, prior to any recognition of content. Most salient image parts are likely to capture image foreground. We propose a novel probabilistic model, shape and figure-ground aware model (sEGmodel) that exploits bottom-up image saliency to compute an informative prior on segment topic assignments. Our model exploits both figure-ground organization in each image separately, as well as feature re-occurrence across the image collection. Since we use image dependent topic prior, during model learning we optimize a conditional likelihood of the image collection given the image bottom-up saliency information. Our discriminative framework can tolerate larger intraclass variability of objects with fewer training data. We iterate between bottom-up figure-ground image organization and model parameter learning by accumulating image statistics from the entire image collection. The model learned influences later image figure-ground labelling. We present results of our approach on diverse datasets showing great improvement over generative probabilistic models that do not exploit image saliency, indicating the suitability of our model for weakly-supervised visual organization.