Content-based image retrieval by a semi-supervised Particle Swarm Optimization

An innovative approach based on an evolutionary stochastic algorithm, namely the Particle Swarm Optimizer (PSO), is proposed in this paper as a solution to the problem of intelligent retrieval of images in large databases. The problem is recast to an optimization one, where a suitable cost function is minimized through a customized PSO. Accordingly, the relevance-feedback is used in order to exploit the information of the user with the aim of both guiding the particles inside the search space and dynamically assigning different weights to the features.