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Salient object detection via region contrast and graph regularization

基于区域对比度和图正则化的显著目标检测

Abstract

Detection of salient objects in an image is now gaining increasing research interest in computer vision community. In this study, a novel region-contrast based saliency detection solution involving three phases is proposed. First, a color-based super-pixels segmentation approach is used to decompose the image into regions. Second, three high-level saliency measures which could effectively characterize the salient regions are evaluated and integrated in an effective manner to produce the initial saliency map. Finally, we construct a pairwise graphical model to encourage that adjacent image regions with similar features take continuous saliency values, thus producing the more perceptually consistent saliency map. We extensively evaluate the proposed method on three public benchmark datasets, and show it can produce promising results when compared to 14 state-of-the-art salient object detection approaches.

摘要

创新点

显著目标检测是计算机视觉领域的一个高度活跃的研究方向。在本文中我们提出了一种基于区域对比度和图正则化的显著目标检测算法。首先, 我们将输入图像分割为感知上相同的超像素区域; 然后, 我们提出三种高级的显著性特征, 这些特征可以很好地表征显著目标, 使用这些特征可以取得准确的显著目标检测表现; 最后, 我们提出了一种基于图模型的显著性优化策略, 该模型通过建模空间上下文关系来优化初始显著图, 最终生成了具有高度空间一致性的显著图。

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Correspondence to Weihai Chen.

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Wu, X., Du, M., Chen, W. et al. Salient object detection via region contrast and graph regularization. Sci. China Inf. Sci. 59, 32104 (2016). https://doi.org/10.1007/s11432-015-5420-9

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Keywords

  • salient object detection
  • region contrast
  • region compactness
  • global distinctness
  • graph regularization

关键词

  • 显著目标检测
  • 区域对比度
  • 区域紧凑度
  • 全局独特性
  • 图正则化