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.

Content-Based Image Retrieval by a Semi-Supervised Particle Swarm Optimization

Broilo, Mattia;Rocca, Paolo;De Natale, Francesco
2008-01-01

Abstract

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.
2008
Proceedings of the 2008 IEEE 10th Workshop on Multimedia Signal Processing
Piscataway, NJ
IEEE
Broilo, Mattia; Rocca, Paolo; De Natale, Francesco
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11572/79106
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