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Ranking Saliency

机译：排名显着

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摘要

Most existing bottom-up algorithms measure the foreground saliency of a pixel or region based on its contrast within a local context or the entire image, whereas a few methods focus on segmenting out background regions and thereby salient objects. Instead of only considering the contrast between salient objects and their surrounding regions, we consider both foreground and background cues in this work. We rank the similarity of image elements with foreground or background cues via graph-based manifold ranking. The saliency of image elements is defined based on their relevances to the given seeds or queries. We represent an image as a multi-scale graph with fine superpixels and coarse regions as nodes. These nodes are ranked based on the similarity to background and foreground queries using affinity matrices. Saliency detection is carried out in a cascade scheme to extract background regions and foreground salient objects efficiently. Experimental results demonstrate the proposed method performs well against the state-of-the-art methods in terms of accuracy and speed. We also propose a new benchmark dataset containing 5,168 images for large-scale performance evaluation of saliency detection methods.
机译：大多数现有的自下而上算法都基于像素或区域在局部上下文或整个图像中的对比度来测量像素或区域的前景显着性，而少数方法则着重于分割背景区域从而突出对象。我们不仅要考虑显着物体与其周围区域之间的对比，还要考虑这项工作中的前景和背景暗示。我们通过基于图的流形排名对图像元素与前景或背景线索的相似性进行排名。图像元素的显着性是根据它们与给定种子或查询的相关性定义的。我们将图像表示为具有精细超像素和粗糙区域作为节点的多尺度图。这些节点是根据与背景和前景查询的相似性（使用亲和力矩阵）进行排序的。显着性检测以级联方案执行，以有效地提取背景区域和前景显着对象。实验结果表明，所提出的方法在准确性和速度方面都与最新方法相比表现良好。我们还提出了一个包含5168张图像的新基准数据集，用于显着性检测方法的大规模性能评估。

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