A wide variety of computer vision applications rely on superpixel or supervoxel algorithms as a preprocessing step. This underlines the overall importance that these approaches have gained in recent years. However, most methods show a lack of temporal consistency or fail in producing temporally stable superpixels. In this paper, we present an approach to generate temporally consistent superpixels for video content. Our method is formulated as a contour-evolving expectation-maximization framework, which utilizes an efficient label propagation scheme to encourage the preservation of superpixel shapes and their relative positioning over time. By explicitly detecting the occlusion of superpixels and the disocclusion of new image regions, our framework is able to terminate and create superpixels whose corresponding image region becomes hidden or newly appears. Additionally, the occluded parts of superpixels are incorporated in the further optimization. This increases the compliance of the superpixel flow with the optical flow present in the scene. Using established benchmark suites, we show that our approach produces highly competitive results in comparison to state-of-the-art streaming-capable supervoxel and superpixel algorithms for video content. This is further shown by comparing the streaming-capable approaches as basis for the task of interactive video segmentation where the proposed approach provides the lowest overall misclassification rate.

中文翻译：

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时间一致超像素的遮挡感知方法


各种计算机视觉应用都依赖超像素或超体素算法作为预处理步骤。这凸显了这些方法近年来所获得的总体重要性。然而，大多数方法缺乏时间一致性或无法产生时间稳定的超像素。在本文中，我们提出了一种为视频内容生成时间一致的超像素的方法。我们的方法被制定为轮廓演化期望最大化框架，它利用有效的标签传播方案来鼓励超像素形状及其随时间的相对定位的保存。通过显式检测超像素的遮挡和新图像区域的解除遮挡，我们的框架能够终止并创建其相应图像区域隐藏或新出现的超像素。此外，超像素被遮挡的部分也被纳入进一步的优化中。这增加了超像素流与场景中存在的光流的合规性。使用已建立的基准套件，我们表明，与视频内容的最先进的具有流媒体功能的超体素和超像素算法相比，我们的方法产生了极具竞争力的结果。通过比较作为交互式视频分割任务基础的支持流媒体的方法，进一步证明了这一点，其中所提出的方法提供了最低的总体错误分类率。