Over-segmentation into superpixels is a very effective dimensionality reduction strategy, enabling fast dense image processing. The main issue of this approach is the inherent irregularity of the image decomposition compared to standard hierarchical multi-resolution schemes, especially when searching for similar neighboring patterns. Several works have attended to overcome this issue by taking into account the region irregularity into their comparison model. Nevertheless, they remain sub-optimal to provide robust and accurate superpixel neighborhood descriptors, since they only compute features within each region, poorly capturing contour information at superpixel borders. In this work, we address these limitations by introducing the dual superpatch, a novel superpixel neighborhood descriptor. This structure contains features computed in reduced superpixel regions, as well as at the interfaces of multiple superpixels to explicitly capture contour structure information. A fast multi-scale non-local matching framework is also introduced for the search of similar descriptors at different resolution levels in an image dataset. The proposed dual superpatch enables to more accurately capture similar structured patterns at different scales, and we demonstrate the robustness and performance of this new strategy on matching and supervised labeling applications.

过度分割为超像素是一种非常有效的降维策略，可实现快速密集的图像处理。与标准的分层多分辨率方案相比，此方法的主要问题是图像分解的固有不规则性，尤其是在搜索相似的相邻图案时。通过将区域不规则性纳入其比较模型，参加了一些工作来克服这个问题。然而，由于它们仅计算每个区域内的特征，因而在超像素边界处捕获轮廓信息的能力很差，因此它们仍然无法提供鲁棒且准确的超像素邻域描述符。在这项工作中，我们通过引入双重超修补程序（一种新颖的超像素邻域描述符）解决了这些限制。此结构包含在缩小的超像素区域以及多个超像素的接口处计算出的特征，以显式捕获轮廓结构信息。还引入了一种快速的多尺度非局部匹配框架，用于搜索图像数据集中不同分辨率级别的相似描述符。提出的双重超级修补程序能够更准确地捕获不同规模的相似结构化模式，并且我们展示了这种新策略在匹配和监督标签应用中的鲁棒性和性能。