论文标题
对象共段的全面显着融合
Comprehensive Saliency Fusion for Object Co-segmentation
论文作者
论文摘要
近年来，由于对预期前景（一组图像中的共享对象）的清晰度，对象进行分割引起了极大的关注。显着融合一直是执行它的有希望的方法之一。但是，先前的作品要么具有相同图像的融合显着图或不同图像的显着性图以提取预期的前景。此外，在大多数情况下，他们依靠手工制作的显着性提取和对应过程。本文重新讨论了该问题，并提出了相同图像和不同图像的显着性图。它还利用深度学习的进步来提取显着性和对应过程。因此，我们称其为全面的显着融合。我们的实验表明，与重要的基准数据集（例如ICOSEG，MSRC和Internet图像）上的先前工作相比，我们的方法获得了众所周知的对象共进行分割结果。
Object co-segmentation has drawn significant attention in recent years, thanks to its clarity on the expected foreground, the shared object in a group of images. Saliency fusion has been one of the promising ways to carry it out. However, prior works either fuse saliency maps of the same image or saliency maps of different images to extract the expected foregrounds. Also, they rely on hand-crafted saliency extraction and correspondence processes in most cases. This paper revisits the problem and proposes fusing saliency maps of both the same image and different images. It also leverages advances in deep learning for the saliency extraction and correspondence processes. Hence, we call it comprehensive saliency fusion. Our experiments reveal that our approach achieves much-improved object co-segmentation results compared to prior works on important benchmark datasets such as iCoseg, MSRC, and Internet Images.
