Though deep learning-based saliency detection methods have achieved gratifying performance recently, the predicted saliency maps still suffer from the boundary challenge. From the perspective of foreground–background separation, this article attempts to extract the edge information of objects by exploiting the difference between different color channels in the RGB color space and establishes a novel multicolor contrast extraction (MCE) mechanism to improve the learning ability of exquisite boundary information of the network. To make full use of the MCE outputs and RGB colors, and well depict and capture the complementary information between them, we devise a novel Siamese densely cooperative fusion (DCF) network (SDFNet) for saliency detection, which consists of two effective components: boundary-directed feature learning (BDFL) and DCF. The BDFL provides joint learning for both MCE and RGB modalities through a Siamese network, while the DCF module is devised for complementary feature discovery, in order to effectively combine the features learned from two modalities. Experiments on five well-known benchmark datasets demonstrate that the proposed method outperforms the state-of-the-art approaches in terms of different evaluation metrics. We provide a detailed analysis of these results and indicate that our joint modeling of MCE and RGB colors helps to better capture the object details, especially in the object boundaries.

中文翻译：

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观察边界：用于显着目标检测的连体密集协作融合


尽管基于深度学习的显着性检测方法最近取得了令人满意的性能，但预测的显着性图仍然面临边界挑战。本文从前背景分离的角度出发，尝试利用RGB颜色空间中不同颜色通道之间的差异来提取物体的边缘信息，并建立一种新颖的多色对比度提取（MCE）机制，以提高精致的学习能力网络的边界信息。为了充分利用 MCE 输出和 RGB 颜色，并很好地描述和捕获它们之间的互补信息，我们设计了一种用于显着性检测的新型连体密集协作融合（DCF）网络（SDFNet），它由两个有效组件组成： -定向特征学习（BDFL）和DCF。 BDFL 通过 Siamese 网络为 MCE 和 RGB 模态提供联合学习，而 DCF 模块则设计用于互补特征发现，以便有效地结合从两种模态学到的特征。对五个著名基准数据集的实验表明，所提出的方法在不同的评估指标方面优于最先进的方法。我们对这些结果进行了详细分析，并表明我们的 MCE 和 RGB 颜色联合建模有助于更好地捕捉对象细节，特别是在对象边界中。