Co-saliency Detection Based on Hierarchical Consistency

As an interesting and emerging topic, co-saliency detection aims at discovering common and salient objects in a group of related images, which is useful to variety of visual media applications. Although a number of approaches have been proposed to address this problem, many of them are designed with the misleading assumption, suboptimal image representation, or heavy supervision cost and thus still suffer from certain limitations, which reduces their capability in the real-world scenarios. To alleviate these limitations, we propose a novel unsupervised co-saliency detection method, which successively explores the hierarchical consistency in the image group including background consistency, high-level and low-level objects consistency in a unified framework. We first design a novel superpixel-wise variational autoencoder (SVAE) network to precisely distinguish the salient objects from the background collection based on the reconstruction errors. Then, we propose a two-stage clustering strategy to explore the multi-level salient objects consistency by using high-level and low-level features separately. Finally, the co-saliency results are refined by applying a CRF based refinement method with the multi-level salient objects consistency. Extensive experiments on three widely datasets show that our method achieves superior or competitive performance compared to the state-of-the-art methods.