# A Dense-Depth Representation for VLAD descriptors in Content-Based Image Retrieval

arXiv: https://arxiv.org/abs/1808.05022 · HTML: https://mlsanity.com/paper/1808.05022/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Aug 2018.

## Built on by (most cited first, 3 of 3)

- [Multiview Scene Graph](https://mlsanity.com/paper/2410.11187/) (2024)
- [Adversarial Learning of Hard Positives for Place Recognition](https://mlsanity.com/paper/2205.03871/) (2022)
- [Multi-modal Loop Closure Detection with Foundation Models in Severely Unstructured Environments](https://mlsanity.com/paper/2511.05404/) (2025)

## Abstract (arXiv)

The recent advances brought by deep learning allowed to improve the performance in image retrieval tasks. Through the many convolutional layers, available in a Convolutional Neural Network (CNN), it is possible to obtain a hierarchy of features from the evaluated image. At every step, the patches extracted are smaller than the previous levels and more representative. Following this idea, this paper introduces a new detector applied on the feature maps extracted from pre-trained CNN. Specifically, this approach lets to increase the number of features in order to increase the performance of the aggregation algorithms like the most famous and used VLAD embedding. The proposed approach is tested on different public datasets: Holidays, Oxford5k, Paris6k and UKB.