Genetic Algorithms for the Optimization of Diffusion Parameters in Content-Based Image Retrieval
This work addresses a domain-specific bottleneck for researchers and practitioners in computer vision by providing an incremental improvement in parameter optimization efficiency.
The paper tackles the problem of manually configuring diffusion parameters in content-based image retrieval by proposing genetic algorithms for optimization, resulting in faster performance compared to brute-force, random-search, and PSO methods on datasets like Oxford5k, Paris6k, and Oxford105k.
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.