Learning to rank for web image retrieval based on genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @InProceedings{Li:2009:ieeeIC-BNMT,
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author = "Piji Li and Jun Ma",
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title = "Learning to rank for web image retrieval based on
genetic programming",
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booktitle = "2nd IEEE International Conference on Broadband Network
Multimedia Technology, IC-BNMT '09",
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year = "2009",
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month = oct,
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pages = "137--142",
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keywords = "genetic algorithms, genetic programming, WIRank, Web
image retrieval, graph theory, image-based feature,
information retrieval system, link structure analysis,
ranking, temporal information, text information,
Internet, graph theory, image retrieval, text
analysis",
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DOI = "doi:10.1109/ICBNMT.2009.5348465",
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abstract = "Ranking is a crucial task in information retrieval
systems. This paper proposes a novel ranking model
named WIRank, which employs a layered genetic
programming architecture to automatically generate an
effective ranking function, by combining various types
of evidences in Web image retrieval, including text
information, image-based features and link structure
analysis. This paper also introduces a new significant
feature to represent images: Temporal information,
which is rarely used in the current information
retrieval systems and applications. The experimental
results show that the proposed algorithms are capable
of learning effective ranking functions for Web image
retrieval. Significant improvement in relevancy
obtained, in comparison to some other well-known
ranking techniques, in terms of MAP, NDCG@n and D@n.",
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notes = "Also known as \cite{5348465}",
- }
Genetic Programming entries for
Piji Li
Jun Ma
Citations