Improving Object Detection Performance with Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @Article{Zhang:2007:IJAIT,
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author = "Mengjie Zhang",
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title = "Improving Object Detection Performance with Genetic
Programming",
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journal = "International Journal on Artificial Intelligence
Tools",
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year = "2007",
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volume = "16",
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number = "5",
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pages = "849--873",
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month = oct,
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keywords = "genetic algorithms, genetic programming, object
recognition, target recognition, fitness function,
program size, two-phase learning, neural networks",
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DOI = "doi:10.1142/S0218213007003576",
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abstract = "This paper describes three developments to improve
object detection performance using genetic programming.
The first investigates three feature sets, the second
investigates a new fitness function, and the third
introduces a two phase learning method using genetic
programming. This approach is examined on three object
detection problems of increasing difficulty and
compared with a neural network approach. The two phase
GP approach with the new fitness function and the local
concentric circular region features achieved the best
results. The results suggest that the concentric
circular pixel statistics are more effective than the
square features for these object detection problems.
The fitness function with program size is more
effective and more efficient than without for these
object detection problems and the evolved genetic
programs using this fitness function are much shorter
and easier to interpret. The two phase GP approach is
more effective and more efficient than the single stage
GP approach, and also more effective than the neural
network approach on these problems using the same set
of features.",
- }
Genetic Programming entries for
Mengjie Zhang
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