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

arXiv: https://arxiv.org/abs/1910.12468 · HTML: https://mlsanity.com/paper/1910.12468/

## Key facts

- Cited by 8 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Oct 2019.

## Built on by (most cited first, 8 of 8)

- [Visual and Object Geo-localization: A Comprehensive Survey](https://mlsanity.com/paper/2112.15202/) (2021)
- [StructVPR: Distill Structural Knowledge with Weighting Samples for Visual Place Recognition](https://mlsanity.com/paper/2212.00937/) (2022)
- [EdgeGaussians -- 3D Edge Mapping via Gaussian Splatting](https://mlsanity.com/paper/2409.12886/) (2024)
- [StructVPR++: Distill Structural and Semantic Knowledge with Weighting Samples for Visual Place Recognition](https://mlsanity.com/paper/2503.06601/) (2025)
- [A Novel Image Descriptor with Aggregated Semantic Skeleton Representation for Long-term Visual Place Recognition](https://mlsanity.com/paper/2202.03677/) (2022)
- [VNI-Net: Vector Neurons-based Rotation-Invariant Descriptor for LiDAR Place Recognition](https://mlsanity.com/paper/2308.12870/) (2023)
- [Weatherproofing Retrieval for Localization with Generative AI and Geometric Consistency](https://mlsanity.com/paper/2402.09237/) (2024)
- [ECT: Fine-grained Edge Detection with Learned Cause Tokens](https://mlsanity.com/paper/2308.03092/) (2023)

## Abstract (arXiv)

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.