多尺度超补丁匹配使用双超像素描述符
Multi-Scale Superpatch Matching using Dual Superpixel Descriptors
AI总结：
本文提出双超补丁描述符，结合超像素区域与边界特征，并引入快速多尺度非局部匹配框架，以更准确捕捉不同尺度的相似结构模式，提升匹配与监督标记性能。
摘要：
过度分割成超像素是一种非常有效的降维策略，能够实现快速的密集图像处理。这种方法的主要问题在于，与标准的分层多分辨率方案相比，图像分解固有的不规则性，尤其是在搜索相似的邻近模式时。一些工作试图通过在其比较模型中考虑区域不规则性来克服这个问题。然而，它们仍然不足以提供稳健且准确的超像素邻域描述符，因为它们仅在每个区域内计算特征，未能很好地捕捉超像素边界处的轮廓信息。在这项工作中，我们通过引入双超补丁（一种新颖的超像素邻域描述符）来解决这些局限性。该结构包含在缩减的超像素区域以及多个超像素的界面处计算的特征，以显式捕捉轮廓结构信息。我们还引入了一个快速的多尺度非局部匹配框架，用于在图像数据集中不同分辨率级别上搜索相似的描述符。所提出的双超补丁能够更准确地捕捉不同尺度下的相似结构模式，并且我们在匹配和监督标记应用中展示了这种新策略的稳健性和性能。
英文摘要：
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 attempted 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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
