Correspondence Driven Saliency Transfer
Correspondence Driven Saliency Transfer
复制标题
对应驱动的显着性转移
DOI：
10.1109/tip.2016.2601784
复制
发表时间：
2016-11-01
影响因子：
10.6
通讯作者：
Porikli, Fatih
中科院分区：
文献类型：
--
作者：
Wang, Wenguan;Shen, Jianbing;Porikli, Fatih
在这篇文章中，我们表明，大量的注释数据集具有巨大的潜力，可以为显著性评估提供强大的先验，而不仅仅是为基准评估服务。为此，我们提出了一种新的图像显著检测方法--显著转移。在给定输入图像的情况下，我们首先从显著标注图像的大型数据库中检索最佳匹配的支持集。然后，我们根据计算的稠密对应关系，通过将支持集标注扭曲到输入图像上来分配过渡显著分数。为了融合上下文，我们采用了两种互补的对应策略：基于场景级分析的全局匹配方案和基于块级别推理的局部匹配方案。然后，我们引入了两种改进措施来进一步优化显著图，并通过探索全局显著结构来应用随机行走和重启来估计前景和背景分配之间的亲和度。在四个公开可用的基准数据集上的大量实验结果表明，所提出的显著度算法的性能始终优于当前最先进的方法。
In this paper, we show that large annotated data sets have great potential to provide strong priors for saliency estimation rather than merely serving for benchmark evaluations. To this end, we present a novel image saliency detection method called saliency transfer. Given an input image, we first retrieve a support set of best matches from the large database of saliency annotated images. Then, we assign the transitional saliency scores by warping the support set annotations onto the input image according to computed dense correspondences. To incorporate context, we employ two complementary correspondence strategies: a global matching scheme based on scene-level analysis and a local matching scheme based on patch-level inference. We then introduce two refinement measures to further refine the saliency maps and apply the random-walk-with-restart by exploring the global saliency structure to estimate the affinity between foreground and background assignments. Extensive experimental results on four publicly available benchmark data sets demonstrate that the proposed saliency algorithm consistently outperforms the current state-of-the-art methods.