Image saliency detection has recently achieved great success due to the development of deep convolutional neural networks. However, most of the existing salient object detection methods cannot identify individual object instances in the detected salient region. In this paper, we present a salient instance segmentation method that produces a saliency map with distinct object instance labels for an input image. Our method consists of three primary steps, i.e., salient region inference, salient object contours detection, and salient object instances identification. For the first two steps, we propose a multiscale saliency refinement network, which generates high-quality salient region masks and salient object contours. For the last step, we propose a morphology algorithm that incorporates detected salient regions and salient object contours to generate promising salient object instance segmentation results. To promote further research and evaluation of salient instance segmentation, we also construct a new database (ILSO-2K) of 2,000 images with pixel-wise salient instance annotations. Experimental results demonstrate that our proposed method is capable of achieving satisfactory performance over six public benchmarks for salient region detection as well as on our new dataset for salient instance segmentation. The source code and proposed dataset will be public available at https://github.com/Kinpzz/MSRNet-CVIU.

由于深度卷积神经网络的发展，图像显着性检测最近获得了巨大的成功。然而，大多数现有的显着物体检测方法不能识别所检测到的显着区域中的单个物体实例。在本文中，我们提出了一种显着实例分割方法，该方法可为输入图像生成具有不同对象实例标签的显着性图。我们的方法包括三个主要步骤，即，显着区域推断，显着物体轮廓检测和显着物体实例识别。对于前两个步骤，我们提出了一个多尺度显着性优化网络，该网络可以生成高质量的显着区域蒙版和显着对象轮廓。对于最后一步，我们提出了一种形态学算法，该算法结合了检测到的显着区域和显着物体轮廓，以生成有希望的显着物体实例分割结果。为了促进对显着实例分割的进一步研究和评估，我们还构建了一个新的数据库（ILSO-2K），该数据库包含2,000个具有逐像素显着实例注释的图像。实验结果表明，我们提出的方法能够在六个公共基准上实现显着区域检测以及在新数据集上进行显着实例分割，从而获得令人满意的性能。源代码和建议的数据集将在https://github.com/Kinpzz/MSRNet-CVIU上公开提供。