Genetic programming applied to image discrimination
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @InCollection{Tackett:1997:HEC,
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author = "Walter Alden Tackett and K. Govinda Char",
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title = "Genetic programming applied to image discrimination",
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booktitle = "Handbook of Evolutionary Computation",
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publisher = "Oxford University Press",
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publisher_2 = "Institute of Physics Publishing",
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year = "1997",
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editor = "Thomas Baeck and David B. Fogel and
Zbigniew Michalewicz",
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chapter = "section G8.2",
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keywords = "genetic algorithms, genetic programming",
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ISBN = "0-7503-0392-1",
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URL = "http://www.crcnetbase.com/isbn/9780750308953",
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URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf",
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DOI = "doi:10.1201/9780367802486",
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size = "10 pages",
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abstract = "Automatic target recognition (ATR) involves the
determination of objects in natural scenes in different
weather conditions and in the presence of various
contaminants. This high degree of variability requires
a flexible system control capable of adapting to the
changing conditions. There is no single set of adaptive
algorithms that would give consistent, reliable results
when subject to the full variety of target conditions.
Although genetic programming (GP) has been successfully
applied to a wide variety of problems its performance
in scaling up to real-world situations needs to be
addressed. In this case study we present the simulation
results of applying GP to ATR through the development
of a processing tree for classification of features
extracted from images: measurements from a set of input
nodes are weighted and combined through linear and
nonlinear operations to form an output response. No
constraints are placed upon size, shape, or order of
processing within the network. This network is used to
classify feature vectors extracted from infra-red
imagery into target/nontarget categories using a
database of 2000 training samples. Performance is
tested against a separate database of 7000 samples.
This represents a significant scaling up from the
problems to which GP has been applied to date. Two
experiments are performed: in the first set, we input
classical statistical image features and minimize
misclassification of target and non-target samples. In
the second set of experiments, GP is allowed to form
its own feature set from primitive intensity
measurements. For purposes of comparison, the same
training and test sets are used to train two other
adaptive classifier systems, the binary tree classifier
and the multilayer perceptron/backpropagation neural
network. The GP network achieves higher performance
with reduced computational requirements. The
contributions of GP building blocks, or subtrees, to
the performance of generated trees are examined.",
- }
Genetic Programming entries for
Walter Alden Tackett
K Govinda Char
Citations