Genetic Programming for Object Detection: A
Two-Phase Approach with an Improved Fitness Function
M. Zhang, U. Bhowan, and B. Ny. Electronic Letters on Computer Vision and Image
Analysis, 6 (1):
27--43(2006)
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 is examined and
compared with a neural network approach on three object
detection problems of increasing difficulty. The
results suggest that the innovations increase both the
effectiveness and the efficiency of the genetic
programming search, and also that the genetic
programming approach outperforms a neural network
approach for the most difficult data set in terms of
the object detection accuracy
%0 Journal Article
%1 Zhang:2006:ELCVIA
%A Zhang, Mengjie
%A Bhowan, Urvesh
%A Ny, Bunna
%D 2006
%J Electronic Letters on Computer Vision and Image
Analysis
%K Artificial Image Intelligence algorithms, analysis, approaches computer genetic networks neural programming, to vis,
%N 1
%P 27--43
%T Genetic Programming for Object Detection: A
Two-Phase Approach with an Improved Fitness Function
%U http://elcvia.cvc.uab.es/public/articles/0601/a2006030-2-art.pdf
%V 6
%X 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 is examined and
compared with a neural network approach on three object
detection problems of increasing difficulty. The
results suggest that the innovations increase both the
effectiveness and the efficiency of the genetic
programming search, and also that the genetic
programming approach outperforms a neural network
approach for the most difficult data set in terms of
the object detection accuracy
@article{Zhang:2006:ELCVIA,
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 is examined and
compared with a neural network approach on three object
detection problems of increasing difficulty. The
results suggest that the innovations increase both the
effectiveness and the efficiency of the genetic
programming search, and also that the genetic
programming approach outperforms a neural network
approach for the most difficult data set in terms of
the object detection accuracy},
added-at = {2008-06-19T17:46:40.000+0200},
author = {Zhang, Mengjie and Bhowan, Urvesh and Ny, Bunna},
biburl = {https://www.bibsonomy.org/bibtex/276ae19a43268c6124809dda774d489a6/brazovayeye},
interhash = {3f30f641c5ffc0625eb7bb2c6b6eb679},
intrahash = {76ae19a43268c6124809dda774d489a6},
issn = {1577-5097},
journal = {Electronic Letters on Computer Vision and Image
Analysis},
keywords = {Artificial Image Intelligence algorithms, analysis, approaches computer genetic networks neural programming, to vis,},
number = 1,
pages = {27--43},
size = {17 pages},
timestamp = {2008-06-19T17:55:38.000+0200},
title = {Genetic Programming for Object Detection: {A}
Two-Phase Approach with an Improved Fitness Function},
url = {http://elcvia.cvc.uab.es/public/articles/0601/a2006030-2-art.pdf},
volume = 6,
year = 2006
}