A Two Phase Genetic Programming Approach to Object Detection
Created by W.Langdon from
gp-bibliography.bib Revision:1.7970
- @TechReport{vuw-CS-TR-04-6,
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author = "Mengjie Zhang and Peter Andreae and Urvesh Bhowan",
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title = "A Two Phase Genetic Programming Approach to Object
Detection",
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institution = "Computer Science, Victoria University of Wellington",
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year = "2004",
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number = "CS-TR-04-6",
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address = "New Zealand",
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keywords = "genetic algorithms, genetic programming, pixel
statistics, false alarm area, program size, two-phase
approach, multiclass object detection",
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URL = "http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-04/CS-TR-04-6.pdf",
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URL = "http://www.mcs.vuw.ac.nz/comp/Publications/CS-TR-04-6.abs.html",
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size = "14 pages",
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abstract = "This paper describes two innovations that improve the
efficiency and effectiveness of a genetic programming
approach to object detection problems. The approach
uses genetic programming to construct object detection
programs that are applied, in a moving window fashion,
to the large images to locate the objects of interest.
The first innovation is to break the GP search into two
phases with the first phase applied to a selected
subset of the training data, and a simplified fitness
function. The second phase is initialised with the
programs from the first phase, and uses the full set of
training data with a complete fitness function to
construct the final detection programs. The second
innovation is to add a program size component to the
fitness function. This approach was applied to three
object detection problems of increasing difficulty. The
results indicate that the innovations increased both
the effectiveness and the efficiency of the genetic
programming search, and also that the genetic
programming approach was more effective than a neural
network approach.",
- }
Genetic Programming entries for
Mengjie Zhang
Peter Andreae
Urvesh Bhowan
Citations