An easy-to-hard learning strategy for within-image co-saliency detection

Within-image co-saliency detection is to detect/highlight the common saliency (similar-appearance salient objects) in a single image. Ideally, it can be solved by detecting each individual salient object first and then comparing them, which is possible for some images with simple representations. However, in practice, this way is not accurate and robust for some images with complex representations. In this paper, we propose an easy-to-hard learning strategy to solve this problem. By directly localizing and comparing salient objects in simple images, superpixel confidences as co-salient objects are inferred by an easy learning method, which provide promising but also noisy supervisions for complex images. Therefore, within-image co-saliency detection in complex images can be modeled as a hard learning problem with noisy labels. A multi-scale Multiple Instance Learning (MIL) model together with a new sampling method is proposed to solve this hard learning problem with noisy labels. Experimental results show that the proposed method achieves the best performance on a public benchmark dataset and two synthetic datasets.