A Two Phase Genetic Programming Approach to Object Detection

Created by W.Langdon from gp-bibliography.bib Revision:1.8129

@TechReport{vuw-CS-TR-04-6,

• author = "Mengjie Zhang and Peter Andreae and Urvesh Bhowan",
• title = "A Two Phase Genetic Programming Approach to Object Detection",
• institution = "Computer Science, Victoria University of Wellington",
• year = "2004",
• number = "CS-TR-04-6",
• address = "New Zealand",
• keywords = "genetic algorithms, genetic programming, pixel statistics, false alarm area, program size, two-phase approach, multiclass object detection",
• URL = "http://www.mcs.vuw.ac.nz/comp/Publications/archive/CS-TR-04/CS-TR-04-6.pdf",
• URL = "http://www.mcs.vuw.ac.nz/comp/Publications/CS-TR-04-6.abs.html",
• size = "14 pages",
• 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 was applied to three object detection problems of increasing difficulty. The results indicate that the innovations increased both the effectiveness and the efficiency of the genetic programming search, and also that the genetic programming approach was more effective than a neural network approach.",

}

Genetic Programming entries for Mengjie Zhang Peter Andreae Urvesh Bhowan

Citations