Optimal Parameter Selection in Image Similarity Evaluation Algorithms Using Particle Swarm Optimization

Image relevance evaluation in conventional Content-Based Image Retrieval (CBIR) researches typically relied on a given criterion. However, it is important that this criterion can be changed according to the use of the image database. This work proposes a framework for tuning the parameters embedded in the relevance evaluation algorithm of a CBIR system, by optimizing them according to the suitability of the retrieved results, using Particle Swarm Optimization (PSO). In the experiments, the parameters that affect the similarity evaluation in a binary shape matching CBIR system were tuned to improve the retrieval ranking score. After tuning the parameters by PSO, it was found that the ranking of the retrieved images were improved according to the given criterion.