Image segmentation, one of the most critical vision tasks, has been studied for many years. Most of the early algorithms are unsupervised methods, which use hand-crafted features to divide the image into many regions. Recently, owing to the great success of deep learning technology, CNNs based methods show superior performance in image segmentation. However, these methods rely on a large number of human annotations, which are expensive to collect. In this paper, we propose a deep unsupervised method for image segmentation, which contains the following two stages. First, a Superpixelwise Autoencoder (SuperAE) is designed to learn the deep embedding and reconstruct a smoothed image, then the smoothed image is passed to generate superpixels. Second, we present a novel clustering algorithm called Deep Superpixel Cut (DSC), which measures the deep similarity between superpixels and formulates image segmentation as a soft partitioning problem. Via backpropagation, DSC adaptively partitions the superpixels into perceptual regions. Experimental results on the BSDS500 dataset demonstrate the effectiveness of the proposed method.
翻译:图像分割是最重要的视觉任务之一, 多年来一直在研究。 大多数早期算法都是不受监督的方法, 使用手工制作的特性将图像分割成许多区域。 最近, 由于深层学习技术的巨大成功, CNN使用的方法在图像分割中表现优异。 然而, 这些方法依赖于大量的人类说明, 收集费用昂贵。 在本文中, 我们建议了一种包含以下两个阶段的图像分割深度不受监督的方法 。 首先, 超级像素自动算法( SuperPerAE) 设计用来学习深层嵌入并重建平滑的图像, 然后光滑的图像传递到生成超级像素。 第二, 我们展示了一种叫做深超像素切( DSC) 的新组合算法, 以测量超像素之间的深度相似性, 并将图像分割成软分割问题 。 Via 反向适应性调整, DSC 将超级像素分割到外观区域 。 BSDS500 数据集的实验结果展示了拟议方法的有效性 。