# Spatio-Semantic ConvNet-Based Visual Place Recognition

arXiv: https://arxiv.org/abs/1909.07671 · HTML: https://mlsanity.com/paper/1909.07671/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Sep 2019.

## Built on by (most cited first, 4 of 4)

- [Patch-NetVLAD: Multi-Scale Fusion of Locally-Global Descriptors for Place Recognition](https://mlsanity.com/paper/2103.01486/) (2021)
- [VLAD-BuFF: Burst-aware Fast Feature Aggregation for Visual Place Recognition](https://mlsanity.com/paper/2409.19293/) (2024)
- [Why-So-Deep: Towards Boosting Previously Trained Models for Visual Place Recognition](https://mlsanity.com/paper/2201.03212/) (2022)
- [Local positional graphs and attentive local features for a data and runtime-efficient hierarchical place recognition pipeline](https://mlsanity.com/paper/2403.10283/) (2024)

## Abstract (arXiv)

We present a Visual Place Recognition system that follows the two-stage format common to image retrieval pipelines. The system encodes images of places by employing the activations of different layers of a pre-trained, off-the-shelf, VGG16 Convolutional Neural Network (CNN) architecture. In the first stage of our method and given a query image of a place, a number of top candidate images is retrieved from a previously stored database of places. In the second stage, we propose an exhaustive comparison of the query image against these candidates by encoding semantic and spatial information in the form of CNN features. Results from our approach outperform by a large margin state-of-the-art visual place recognition methods on five of the most commonly used benchmark datasets. The performance gain is especially remarkable on the most challenging datasets, with more than a twofold recognition improvement with respect to the latest published work.