街景中基于图学习的图像块匹配
Image Patch-Matching with Graph-Based Learning in Street Scenes
AI总结：
本文针对街景图像块匹配提出基于空间图的联合特征与度量学习模型，利用图像块邻域关系提升匹配性能，并在多个数据集上取得最优结果。
摘要：
将车载摄像头实时拍摄图像中的地标图像块与图像数据库中的地标图像块进行匹配，在自动驾驶的各种计算机感知任务中发挥着重要作用。当前方法侧重于对感兴趣区域进行局部匹配，并未考虑图像块之间的空间邻域关系，而这些图像块通常对应环境中的物体。在本文中，我们构建了一个空间图，其中图的顶点对应图像块，边捕获空间邻域信息。我们提出了一种结合图学习的联合特征与度量学习模型。我们为基于图的损失提供了理论基础，表明在匹配和未匹配对条件下分布之间的信息距离在我们的框架下被最大化。我们使用多个街景数据集评估了我们的模型，并证明我们的方法取得了最先进的匹配结果。
英文摘要：
Matching landmark patches from a real-time image captured by an on-vehicle camera with landmark patches in an image database plays an important role in various computer perception tasks for autonomous driving. Current methods focus on local matching for regions of interest and do not take into account spatial neighborhood relationships among the image patches, which typically correspond to objects in the environment. In this paper, we construct a spatial graph with the graph vertices corresponding to patches and edges capturing the spatial neighborhood information. We propose a joint feature and metric learning model with graph-based learning. We provide a theoretical basis for the graph-based loss by showing that the information distance between the distributions conditioned on matched and unmatched pairs is maximized under our framework. We evaluate our model using several street-scene datasets and demonstrate that our approach achieves state-of-the-art matching results.
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。
