Genetic programming applied to image discrimination

Created by W.Langdon from gp-bibliography.bib Revision:1.7615

@InCollection{Tackett:1997:HEC,

• author = "Walter Alden Tackett and K. Govinda Char",
• title = "Genetic programming applied to image discrimination",
• booktitle = "Handbook of Evolutionary Computation",
• publisher = "Oxford University Press",
• publisher_2 = "Institute of Physics Publishing",
• year = "1997",
• editor = "Thomas Baeck and David B. Fogel and Zbigniew Michalewicz",
• chapter = "section G8.2",
• keywords = "genetic algorithms, genetic programming",
• ISBN = "0-7503-0392-1",
• URL = "http://www.crcnetbase.com/isbn/9780750308953",
• URL = "http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.375.6494.pdf",
• DOI = "doi:10.1201/9780367802486",
• size = "10 pages",
• abstract = "Automatic target recognition (ATR) involves the determination of objects in natural scenes in different weather conditions and in the presence of various contaminants. This high degree of variability requires a flexible system control capable of adapting to the changing conditions. There is no single set of adaptive algorithms that would give consistent, reliable results when subject to the full variety of target conditions. Although genetic programming (GP) has been successfully applied to a wide variety of problems its performance in scaling up to real-world situations needs to be addressed. In this case study we present the simulation results of applying GP to ATR through the development of a processing tree for classification of features extracted from images: measurements from a set of input nodes are weighted and combined through linear and nonlinear operations to form an output response. No constraints are placed upon size, shape, or order of processing within the network. This network is used to classify feature vectors extracted from infra-red imagery into target/nontarget categories using a database of 2000 training samples. Performance is tested against a separate database of 7000 samples. This represents a significant scaling up from the problems to which GP has been applied to date. Two experiments are performed: in the first set, we input classical statistical image features and minimize misclassification of target and non-target samples. In the second set of experiments, GP is allowed to form its own feature set from primitive intensity measurements. For purposes of comparison, the same training and test sets are used to train two other adaptive classifier systems, the binary tree classifier and the multilayer perceptron/backpropagation neural network. The GP network achieves higher performance with reduced computational requirements. The contributions of GP building blocks, or subtrees, to the performance of generated trees are examined.",

}

Genetic Programming entries for Walter Alden Tackett K Govinda Char

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