%0 Journal Article %1 Zhang:2003:JASP %A Zhang, Mengjie %A Ciesielski, Victor B. %A Andreae, Peter %D 2003 %J EURASIP Journal on Applied Signal Processing %K algorithms, computer detection, genetic learning, machine networks, neural object programming, recognition, target vision %N 8 %P 841--859 %R doi:10.1155/S1110865703303063 %T A Domain-independent Window approach to Multiclass Object Detection using Genetic Programming %U http://www.mcs.vuw.ac.nz/~pondy/eurasip2003.pdf %V 2003 %X a domain-independent approach to the use of genetic programming for object detection problems in which the locations of small objects of multiple classes in large images must be found. The evolved program is scanned over the large images to locate the objects of interest. The paper develops three terminal sets based on domain-independent pixel statistics and considers two different function sets. The fitness function is based on the detection rate and the false alarm rate. We have tested the method on three object detection problems of increasing difficulty. This work not only extends genetic programming to multiclass-object detection problems, but also shows how to use a single evolved genetic program for both object classification and localisation. The object classification map developed in this approach can be used as a general classification strategy in genetic programming for multiple-class classification problems.

@article{Zhang:2003:JASP, abstract = {a domain-independent approach to the use of genetic programming for object detection problems in which the locations of small objects of multiple classes in large images must be found. The evolved program is scanned over the large images to locate the objects of interest. The paper develops three terminal sets based on domain-independent pixel statistics and considers two different function sets. The fitness function is based on the detection rate and the false alarm rate. We have tested the method on three object detection problems of increasing difficulty. This work not only extends genetic programming to multiclass-object detection problems, but also shows how to use a single evolved genetic program for both object classification and localisation. The object classification map developed in this approach can be used as a general classification strategy in genetic programming for multiple-class classification problems.}, added-at = {2008-06-19T17:46:40.000+0200}, author = {Zhang, Mengjie and Ciesielski, Victor B. and Andreae, Peter}, biburl = {https://www.bibsonomy.org/bibtex/299d8143cc33a5b2adce0bfca847144db/brazovayeye}, doi = {doi:10.1155/S1110865703303063}, email = {mengjie@mcs.vuw.ac.nz}, interhash = {9db39ac57d6c48be032692a02b4284ed}, intrahash = {99d8143cc33a5b2adce0bfca847144db}, issn = {1110-8657}, journal = {{EURASIP} Journal on Applied Signal Processing}, keywords = {algorithms, computer detection, genetic learning, machine networks, neural object programming, recognition, target vision}, month = {July}, note = {Special Issue on Genetic and Evolutionary Computation for Signal Processing and Image Analysis}, notes = {Special Issue on genetic and evolutionary computation for signal processing and image analysis http://asp.hindawi.com/volume-2003/issue-8.html European Association for Speech, Signal and Image Processing (EURASIP) asp@asp.hindawi.com 31 Aug 2003 problems reading PDF}, number = 8, pages = {841--859}, timestamp = {2008-06-19T17:55:32.000+0200}, title = {A Domain-independent Window approach to Multiclass Object Detection using Genetic Programming}, url = {http://www.mcs.vuw.ac.nz/~pondy/eurasip2003.pdf}, volume = 2003, year = 2003 }