Abstract
This paper describes a domain independent approach to
the use of neural networks (NNs) and genetic
programming (GP) for object detection problems. Instead
of using high level features for a particular task,
this approach uses domain independent pixel statistics
for object detection. The paper first compares an NN
method and a GP method on four image data sets
providing object detection problems of increasing
difficulty. The results show that the GP method
performs better than the NN method on these problems
but still produces a large number of false alarms on
the difficult problem and computation cost is still
high. To deal with these problems, we develop a new
method called GP-refine that uses a two stage learning
process. The results suggest that the new GP method
further improves object detection performance on the
difficult object detection task.
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