Open AccessGenetic Programming for Object Detection: A Two-Phase Approach with an Improved Fitness Function
Author(s) -
Mengjie Zhang,
Urvesh Bhowan,
Buny
Publication year - 2007
Publication title -
elcvia. electronic letters on computer vision and image analysis
Language(s) - English
Resource type - Journals
ISSN - 1577-5097
DOI - 10.5565/rev/elcvia.135
Subject(s) - genetic programming , fitness function , construct (python library) , computer science , object (grammar) , artificial intelligence , set (abstract data type) , genetic algorithm , artificial neural network , machine learning , function (biology) , object oriented programming , object detection , data mining , pattern recognition (psychology) , programming language , evolutionary biology , biology
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