协同视觉显著性检测方法综述

协同视觉显著性检测是视觉注意力计算领域中一个快速发展的新兴分支，致力于检测多幅相关场景图像中的公共显著目标，在各种计算机视觉任务中有广泛应用.考虑到特征提取策略的设计是协同视觉显著性检测当前研究的重点，本文首先根据特征提取策略的不同对现有的协同视觉显著检测方法进行了分类介绍和定性分析.其次，通过在5个公开数据库上的主观和定量对比，对各流行算法的性能进行了评估，分析了特征提取策略对算法性能的影响以及各数据库的复杂度，并验证了协同视觉显著性检测和视觉显著性检测的区别.最后，对本文工作进行了总结，并对当前研究中存在的问题和未来的研究工作进行了讨论.

A Review of Co-saliency Detection

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.