Distance-based relevance feedback using a hybrid interactive genetic algorithm for image retrieval

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M. Arevalillo-Herrez, F. J. Ferri, and S. Moreno-Picot: Distance-based relevance feedback using a hybrid interactive genetic algorithm for image retrieval. Applied Soft Computing, 11(2):1782-1791, 2011.




Content-based image retrieval (CBIR) systems aim to provide a means to find pictures in large repositories without using any other information except the own content of the images, which is usually represented as a feature vector extracted from low-level descriptors. This paper describes a CBIR algorithm which combines relevance feedback, evolutionary computation concepts and distance-based learning in an attempt to reduce the existing gap between the high level semantic content of the images and the information provided by their low-level descriptors. In particular, a framework which is independent from the particular features used is presented. The effect of different crossover strategies and mutation rates is evaluated, and the performance of the technique is compared to that of other existing algorithms, obtaining considerably better and very promising results.

Extended Abstract


title = "Distance-based relevance feedback using a hybrid interactive genetic algorithm for image retrieval ",
journal = "Applied Soft Computing ",
volume = "11",
number = "2",
pages = "1782 - 1791",
year = "2011",
note = "The Impact of Soft Computing for the Progress of Artificial Intelligence ",
issn = "1568-4946",
doi = "http://dx.doi.org/10.1016/j.asoc.2010.05.022",
url = "http://www.sciencedirect.com/science/article/pii/S1568494610001237 http://de.evo-art.org/index.php?title=Distance-based_relevance_feedback_using_a_hybrid_interactive_genetic_algorithm_for_image_retrieval",
author = "Miguel Arevalillo-Herráez and Francesc J. Ferri and Salvador Moreno-Picot",
keywords = "CBIR",
keywords = "Image retrieval",
keywords = "Relevance feedback",
keywords = "Evolutionary computation",
keywords = "Genetic algorithms "

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