With the goal of discovering the common and salient objects from the given image group, co-saliency detection has received tremendous research interest in recent years. However, as most of the existing co-saliency detection methods are performed based on the assumption that all the images in the given image group should contain co-salient objects in only one category, they can hardly be applied in practice, particularly for the large-scale image set obtained from the Internet. To address this problem, this paper revisits the co-saliency detection task and advances its development into a new phase, where the problem setting is generalized to allow the image group to contain objects in arbitrary number of categories and the algorithms need to simultaneously detect multi-class co-salient objects from such complex data. To solve this new challenge, we decompose it into two sub-problems, i.e., how to identify subgroups of relevant images and how to discover relevant co-salient objects from each subgroup, and propose a novel co-saliency detection framework to correspondingly address the two sub-problems via two-stage multi-view spectral rotation co-clustering. Comprehensive experiments on two publically available benchmarks demonstrate the effectiveness of the proposed approach. Notably, it can even outperform the state-of-the-art co-saliency detection methods, which are performed based on the image subgroups carefully separated by the human labor.

Revisiting Co-Saliency Detection: A Novel Approach Based on Two-Stage Multi-View Spectral Rotation Co-clustering