%0 Report %1 vuw-CS-TR-06-8 %A Zhang, Mengjie %A Lett, Malcolm %C New Zealand %D 2006 %K Fitness algorithms, classification, detection, examples, function, genetic localisation object programming, recognition, training %N CS-TR-06-8 %T Improving Fitness Function and Optimising Training Data in GP for Object Detection %U http://www.mcs.vuw.ac.nz/comp/Publications/CS-TR-06-8.abs.html %X the refinement of a fitness function and the optimisation of training data in genetic programming (GP) for object detection particularly object localisation problems. The fitness function uses the weighted F-measure of a genetic program and considers the localisation fitness values of the detected object locations. To investigate the training data with this fitness function, we categorise the training data into four types: exact centre, close to centre, include centre, and background. The approach is examined and compared with an existing fitness function on three object detection problems of increasing difficulty. The results suggest that the new fitness function outperforms the old one by producing far fewer false alarms and spending much less training time and that the first two types of the training examples contain most of the useful information for object detection. The results also suggest that the complete background type of data can be removed from the training set.

@techreport{vuw-CS-TR-06-8, abstract = {the refinement of a fitness function and the optimisation of training data in genetic programming (GP) for object detection particularly object localisation problems. The fitness function uses the weighted F-measure of a genetic program and considers the localisation fitness values of the detected object locations. To investigate the training data with this fitness function, we categorise the training data into four types: exact centre, close to centre, include centre, and background. The approach is examined and compared with an existing fitness function on three object detection problems of increasing difficulty. The results suggest that the new fitness function outperforms the old one by producing far fewer false alarms and spending much less training time and that the first two types of the training examples contain most of the useful information for object detection. The results also suggest that the complete background type of data can be removed from the training set.}, added-at = {2008-06-19T17:46:40.000+0200}, address = {New Zealand}, author = {Zhang, Mengjie and Lett, Malcolm}, biburl = {https://www.bibsonomy.org/bibtex/2c781de5e447789ffa3b2ced6e0f1e927/brazovayeye}, institution = {Computer Science, Victoria University of Wellington}, interhash = {129b1a8168600d2a9b636cde698599df}, intrahash = {c781de5e447789ffa3b2ced6e0f1e927}, keywords = {Fitness algorithms, classification, detection, examples, function, genetic localisation object programming, recognition, training}, number = {CS-TR-06-8}, timestamp = {2008-06-19T17:55:37.000+0200}, title = {Improving Fitness Function and Optimising Training Data in {GP} for Object Detection}, url = {http://www.mcs.vuw.ac.nz/comp/Publications/CS-TR-06-8.abs.html}, year = 2006 }