Co-saliency detection is a new branch with the rapid development in the field of visual attention

which concerns the detection of the common salient objects from multiple relevant scene images

and can be widely used in various computer vision tasks.Considering the key point of current research is the design of feature extraction strategy

the existing co-saliency detection methods are firstly summarized and qualitatively analyzed according to the different feature extraction strategies in this paper.Subsequently

based on the subjective and quantitative comparisons in the five open datasets

the performance of the state-of-the-art algorithms is evaluated

the influence of the feature extraction strategy on the performance of algorithms and the complexity of the datasets is analyzed

and the difference of co-saliency detection and saliency detection is also verified.Finally

the conclusion of this paper are presented

the problems of current research and the future development are also discussed.