Evolutionary image retrieval

Author

Broilo, M. ; De Natale, F.G.B.

Author_Institution

DISI, Univ. of Trento, Trento, Italy

fYear

2009

fDate

7-10 Nov. 2009

Firstpage

1845

Lastpage

1848

Abstract

The paper presents a method for content-based image retrieval based on an evolutionary algorithm. Stochastic approaches have been applied with success in several optimization problems thanks to their capability to explore the solution space, in particular in complex, multidimensional spaces, avoiding local maxima of the target function. Here, we show how a Particle Swarm Optimization algorithm appropriately designed to exploit the user feedback in CBIR may outperform traditional Relevance Feedback approaches, showing a much higher precision/recall thanks to the capability of navigating the feature space and to move the swarm towards the most appropriate image cluster.

Keywords

content-based retrieval; evolutionary computation; image retrieval; particle swarm optimisation; relevance feedback; content-based image retrieval; evolutionary algorithm; evolutionary image retrieval; feature space; image cluster; multidimensional space; optimization problem; particle swarm optimization algorithm; relevance feedback; solution space; target function; user feedback; Algorithm design and analysis; Clustering algorithms; Content based retrieval; Evolutionary computation; Feedback; Image retrieval; Multidimensional systems; Particle swarm optimization; Space exploration; Stochastic processes; Content-based Image Retrieval; Particle Swarm Optimization; Relevance Feedback;

fLanguage

English

Publisher

ieee

Conference_Titel

Image Processing (ICIP), 2009 16th IEEE International Conference on

Conference_Location

Cairo

ISSN

1522-4880

Print_ISBN

978-1-4244-5653-6

Electronic_ISBN

1522-4880

Type

conf

DOI

10.1109/ICIP.2009.5413574

Filename

5413574