Cluster-based co-saliency detection.
Cluster-based co-saliency detection.
复制标题
基于群集的共同检测。
DOI：
10.1109/tip.2013.2260166
复制
发表时间：
2013-10
期刊：
影响因子：
--
通讯作者：
Tu Z
中科院分区：
文献类型：
--
作者：
Fu H;Cao X;Tu Z
共显着性用于发现多幅图像上的共同显着性，这是一个相对未开发的领域。在本文中，我们介绍了一种新的基于聚类的共显着性检测算法。多个图像之间的全局对应关系在聚类过程中被隐式地学习。设计了三种视觉注意线索：对比度、空间和对应，以有效地衡量集群的显着性。通过融合单幅图像显著性和多幅图像显著性来生成最终的共显著性图。我们的方法的优点是大多是自底向上的，没有沉重的学习，并具有简单，通用，高效，有效的属性。在各种基准数据集上的定量和定性实验结果表明，该方法优于竞争的共显着性方法，我们的方法在单图像上也优于大多数最先进的显着性检测方法。此外，我们将共同显着性方法应用于四个视觉应用：共同分割，鲁棒图像距离，弱监督学习和视频前景检测，这表明了共同显着性图的潜在用途。
Co-saliency is used to discover the common saliency on the multiple images, which is a relatively under-explored area. In this paper, we introduce a new cluster-based algorithm for co-saliency detection. Global correspondence between the multiple images is implicitly learned during the clustering process. Three visual attention cues: contrast, spatial, and corresponding, are devised to effectively measure the cluster saliency. The final co-saliency maps are generated by fusing the single image saliency and multi-image saliency. The advantage of our method is mostly bottom-up without heavy learning, and has the property of being simple, general, efficient, and effective. Quantitative and qualitative experimental results on a variety of benchmark datasets demonstrate the advantages of the proposed method over the competing co-saliency methods, and our method on single image also outperforms most the state-of-the-art saliency detection methods. Furthermore, we apply the co-saliency method on four vision applications: co-segmentation, robust image distance, weakly supervised learning, and video foreground detection, which demonstrate the potential usages of the co-saliency map.