  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.
