Most of the research effort on image-based place recognition is designed for urban environments. In bucolic environments such as natural scenes with low texture and little semantic content, the main challenge is to handle the variations in visual appearance across time such as illumination, weather, vegetation state or viewpoints. The nature of the variations is different and this leads to a different approach to describing a bucolic scene. We introduce a global image descriptor computed from its semantic and topological information. It is built from the wavelet transforms of the image semantic edges. Matching two images is then equivalent to matching their semantic edge descriptors. We show that this method reaches state-of-the-art image retrieval performance on two multi-season environment-monitoring datasets: the CMU-Seasons and the Symphony Lake dataset. It also generalises to urban scenes on which it is on par with the current baselines NetVLAD and DELF.

该研究介绍了一种针对自然场景而设计的全局图像描述符，并利用其语义和拓扑信息构建，以应对光照、天气、植被状态或视角等各种时间变化对图像造成的影响，并在两个多季节环境监测数据集上达到了最新图像检索算法的最佳表现。

基于视觉边缘特征的田园环境季节间图像匹配