Learning to rank for content-based image retrieval
Created by W.Langdon from
gp-bibliography.bib Revision:1.8081
- @InProceedings{Faria2010MIR,
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author = "Fabio Augusto Faria and Adriano Veloso and
Humberto {Mossri de Almeida} and Eduardo Valle and
Ricardo {da S. Torres} and Marcos Andre Goncalves and
Wagner {Meira Jr.}",
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title = "Learning to rank for content-based image retrieval",
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booktitle = "Multimedia Information Retrieval (MIR)",
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year = "2010",
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pages = "285--294",
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address = "Philadelphia, Pennsylvania, USA",
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keywords = "genetic algorithms, genetic programming, SVM",
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bibsource = "DBLP, http://dblp.uni-trier.de",
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URL = "http://doi.acm.org/10.1145/1743384.1743434",
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DOI = "doi:10.1145/1743384.1743434",
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abstract = "In Content-based Image Retrieval (CBIR), accurately
ranking the returned images is of paramount importance,
since users consider mostly the topmost results. The
typical ranking strategy used by many CBIR systems is
to employ image content descriptors, so that returned
images that are most similar to the query image are
placed higher in the rank. While this strategy is well
accepted and widely used, improved results may be
obtained by combining multiple image descriptors. In
this paper we explore this idea, and introduce
algorithms that learn to combine information coming
from different descriptors. The proposed learning to
rank algorithms are based on three diverse learning
techniques: Support Vector Machines (CBIR-SVM), Genetic
Programming (CBIR-GP), and Association Rules (CBIR-AR).
Eighteen image content descriptors(colour, texture, and
shape information) are used as input and provided as
training to the learning algorithms. We performed a
systematic evaluation involving two complex and
heterogeneous image databases (Corel e Caltech) and two
evaluation measures (Precision and MAP). The empirical
results show that all learning algorithms provide
significant gains when compared to the typical ranking
strategy in which descriptors are used in isolation. We
concluded that, in general, CBIR-AR and CBIR-GP
outperforms CBIR-SVM. A fine-grained analysis revealed
the lack of correlation between the results provided by
CBIR-AR and the results provided by the other two
algorithms, which indicates the opportunity of an
advantageous hybrid approach.",
- }
Genetic Programming entries for
Fabio Augusto Faria
Adriano Veloso
Humberto Mossri de Almeida
Eduardo Valle
Ricardo da Silva Torres
Marcos Andre Goncalves
Wagner Meira
Citations