Image-Based Place Recognition on Bucolic Environment Across Seasons From Semantic Edge Description

TL;DR

This paper proposes a novel global image descriptor based on semantic edges for place recognition in bucolic environments, effectively handling seasonal and environmental variations, and performs competitively in urban scenes.

Contribution

It introduces a semantic edge-based descriptor using wavelet transforms, tailored for natural scenes with seasonal changes, achieving state-of-the-art results in bucolic environments.

Findings

Achieves state-of-the-art performance on CMU-Seasons and Symphony Lake datasets.

Generalizes well to urban scenes, matching current baselines.

Effective in handling seasonal and environmental variations.

Abstract

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.