# Genetic Algorithms for the Optimization of Diffusion Parameters in Content-Based Image Retrieval

arXiv: https://arxiv.org/abs/1908.06896 · HTML: https://mlsanity.com/paper/1908.06896/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Aug 2019.

## Built on by (most cited first, 2 of 2)

- [GreenStableYolo: Optimizing Inference Time and Image Quality of Text-to-Image Generation](https://mlsanity.com/paper/2407.14982/) (2024)
- [Enhancing Visual Re-ranking through Denoising Nearest Neighbor Graph via Continuous CRF](https://mlsanity.com/paper/2412.13875/) (2024)

## Abstract (arXiv)

Several computer vision and artificial intelligence projects are nowadays exploiting the manifold data distribution using, e.g., the diffusion process. This approach has produced dramatic improvements on the final performance thanks to the application of such algorithms to the kNN graph. Unfortunately, this recent technique needs a manual configuration of several parameters, thus it is not straightforward to find the best configuration for each dataset. Moreover, the brute-force approach is computationally very demanding when used to optimally set the parameters of the diffusion approach. We propose to use genetic algorithms to find the optimal setting of all the diffusion parameters with respect to retrieval performance for each different dataset. Our approach is faster than others used as references (brute-force, random-search and PSO). A comparison with these methods has been made on three public image datasets: Oxford5k, Paris6k and Oxford105k.