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

TL;DR

This paper introduces a genetic algorithm-based method to efficiently optimize diffusion parameters in content-based image retrieval, outperforming brute-force and other heuristic methods in speed and effectiveness across multiple datasets.

Contribution

The paper presents a novel application of genetic algorithms to automatically tune diffusion parameters in image retrieval, reducing computational cost and improving performance.

Findings

Genetic algorithms outperform brute-force, random-search, and PSO in speed.

Optimized parameters improve retrieval accuracy.

Method tested successfully on three public datasets.

Abstract

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.