Semantic feature extraction using genetic programming in image retrieval
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @InProceedings{Li:2004:ICPR,
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author = "Qingyong Li and Hong Hu and Zhongzhi Shi",
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title = "Semantic feature extraction using genetic programming
in image retrieval",
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booktitle = "Proceedings of the 17th International Conference on
Pattern Recognition, ICPR 2004",
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year = "2004",
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volume = "1",
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pages = "648--651",
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month = aug,
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keywords = "genetic algorithms, genetic programming, content-based
retrieval, feature extraction, image retrieval, image
texture, visual perception Tamura texture model,
content based image retrieval, human visual perception,
linguistic expression, semantic feature extraction,
texture extraction, visual feature extraction",
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DOI = "doi:10.1109/ICPR.2004.1334248",
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size = "4 pages",
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abstract = "One of the big hurdles facing current content-based
image retrieval (CBIR) is the semantic gap between the
low-level visual features and the high-level semantic
features. We proposed an approach to describe and
extract the global texture semantic features. According
to the Tamura texture model, we use the linguistic
variable to describe the texture semantics, so it
becomes possible to depict the image in linguistic
expression such as coarse, fine. We use genetic
programming to simulate the human visual perception and
extract the semantic features value. Our experiments
show that the semantic features have good accordance
with the human perception, and also have good retrieval
performance. In some extent, our approach bridges the
semantic gap in CBIR.",
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notes = "also known as \cite{1334248}",
- }
Genetic Programming entries for
Qingyong Li
Hong Hu
Zhongzhi Shi
Citations