caa a22 4500 605470219 CHVBK 20210128100326.0 cr unu---uuuuu 210128e20150801xx s 000 0 eng 10.1007/s00500-014-1432-4 doi (NATIONALLICENCE)springer-10.1007/s00500-014-1432-4 Distribution-based invariant feature construction using genetic programming for edge detection [Elektronische Daten] [Wenlong Fu, Mark Johnston, Mengjie Zhang] In edge detection, constructing features with rich responses on different types of edges is a challenging problem. Genetic programming (GP) has been previously employed to construct features. Normally, the values of the features constructed by GP are calculated from raw observations. Some existing work has considered the distributions of the raw observations, but these features only poorly indicate class label probabilities. To construct features with rich responses on different types of edges, the distributions of the observations from GP programs are investigated in this study. The values of the constructed features are obtained from estimated distributions, rather than directly using the observations. These features themselves indicate probabilities for the target labels. Basic rotation-invariant features from gradients, image quality, and local histograms are used to construct new composite features. The results show that the invariant features constructed by GP combine advantages from the basic features, reduce drawbacks from basic features alone, and improve the detection performance. In terms of the quantitative and qualitative evaluations, features constructed by GP with distribution estimation are better than the combinations from a Bayesian model and a linear support vector machine approach. Springer-Verlag Berlin Heidelberg, 2014 Edge detection nationallicence Genetic programming nationallicence Distribution estimation nationallicence Feature extraction nationallicence Fu Wenlong School of Mathematics, Statistics and Operations Research, Victoria University of Wellington, PO Box 600, Wellington, New Zealand aut Johnston Mark School of Mathematics, Statistics and Operations Research, Victoria University of Wellington, PO Box 600, Wellington, New Zealand aut Zhang Mengjie School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington, New Zealand aut Soft Computing Springer Berlin Heidelberg 19/8(2015-08-01), 2371-2389 1432-7643 19:8<2371 2015 19 500 https://doi.org/10.1007/s00500-014-1432-4 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s00500-014-1432-4 text/html Onlinezugriff via DOI NATIONALLICENCE 700 1- Fu Wenlong School of Mathematics, Statistics and Operations Research, Victoria University of Wellington, PO Box 600, Wellington, New Zealand aut NATIONALLICENCE 700 1- Johnston Mark School of Mathematics, Statistics and Operations Research, Victoria University of Wellington, PO Box 600, Wellington, New Zealand aut NATIONALLICENCE 700 1- Zhang Mengjie School of Engineering and Computer Science, Victoria University of Wellington, PO Box 600, Wellington, New Zealand aut NATIONALLICENCE 773 0- Soft Computing Springer Berlin Heidelberg 19/8(2015-08-01), 2371-2389 1432-7643 19:8<2371 2015 19 500