Saliency Region Detection Based on Markov Absorption Probabilities
Saliency Region Detection Based on Markov Absorption Probabilities
复制标题
DOI：
10.1109/tip.2015.2403241
复制
发表时间：
2015-02
影响因子：
10.6
通讯作者：
Jingang Sun;Huchuan Lu;Xiuping Liu
中科院分区：
文献类型：
--
作者：
Jingang Sun;Huchuan Lu;Xiuping Liu
本文利用显著性检测与马尔可夫吸收概率之间的关系，提出了一种自下而上的显著性目标检测方法。首先，我们以部分图像边界为背景，通过加权图上的马尔可夫吸收概率计算出初步的显著性图。与大多数现有的基于背景先验的方法将所有图像边界作为背景不同，为了简单起见，我们只使用左侧和顶部作为背景。每个元素的显著性定义为与其最相似的若干个左侧和顶部虚拟边界节点对应的吸收概率之和。其次，对图像元素与从初步显著性图中提取的前景线索的相关性进行排序，得到较好的结果，可以有效地强调背景下的目标，其计算过程与第一阶段相似，但与前一阶段有本质区别。最后，利用基于内容的扩散机制、超像素抑制函数和引导滤波三种优化技术对第二阶段推广的显著性图进行进一步修正，证明了这三种优化技术的有效性和互补性。对四个公开可用的基准数据集的定性和定量评估表明，与17种最先进的方法相比，所提出的方法具有稳健性和效率。
In this paper, we present a novel bottom-up salient object detection approach by exploiting the relationship between the saliency detection and the Markov absorption probability. First, we calculate a preliminary saliency map by the Markov absorption probability on a weighted graph via partial image borders as background prior. Unlike most of the existing background prior-based methods which treated all image boundaries as background, we only use the left and top sides as background for simplicity. The saliency of each element is defined as the sum of the corresponding absorption probability by several left and top virtual boundary nodes, which are most similar to it. Second, a better result is obtained by ranking the relevance of the image elements with foreground cues extracted from the preliminary saliency map, which can effectively emphasize the objects against the background, whose computation is processed similarly as that in the first stage and yet substantially different from the former one. At last, three optimization techniques - content-based diffusion mechanism, superpixelwise depression function, and guided filter - are utilized to further modify the saliency map generalized at the second stage, which is proved to be effective and complementary to each other. Both qualitative and quantitative evaluations on four publicly available benchmark data sets demonstrate the robustness and efficiency of the proposed method against 17 state-of-the-art methods.