Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Bao P, Zhang L, Wu X (2005) Canny edge detection enhancement by scale multiplication. IEEE Trans Pattern Anal Mach Intell 27(9):1485–1490
Basu M (2002) Gaussian-based edge-detection methods: a survey. IEEE Trans Syst Man Cybern Part C Appl Rev 32(3):252–260
Bhowan U, Johnston M, Zhang M (2011) Developing new fitness functions in genetic programming for classification with unbalanced data. IEEE Trans Syst Man Cybern Part B Cybern 42(2):406–421
Burges CJC (1998) A tutorial on support vector machines for pattern recognition. Data Min Knowl Discov 2(2):121–167
Canny J (1986) A computational approach to edge detection. IEEE Trans Pattern Anal Mach Intell 8(6):679–698
Dollar P, Tu Z, Belongie S (2006) Supervised learning of edges and object boundaries. Proc IEEE Comput Soc Conf Comput Vision Pattern Recogn 2:1964–1971
Duda RO, Hart PE, Stork DG (2000) Pattern classification, 2nd edn. Wiley-Interscience, New York
Espejo PG, Ventura S, Herrera F (2010) A survey on the application of genetic programming to classification. IEEE Trans Syst Man Cybern Part C Appl Rev 40:121–144
Fernández-García N, Carmona-Poyato A, Medina-Carnicer R, Madrid-Cuevas F (2008) Automatic generation of consensus ground truth for the comparison of edge detection techniques. Image Vision Comput 26(4):496–511
Fu W, Johnston M, Zhang M (2011a) Analysis of diagonal derivatives in edge detectors evolved by genetic programming. In: Proceedings of the twenty-sixth international conference on image and vision computing New Zealand, pp 345–350
Fu W, Johnston M, Zhang M (2011b) A hybrid particle swarm optimisation with differential evolution approach to image segmentation. In: Proceedings of the international conference on applications of evolutionary computation: evoapplications, pp 173–182
Fu W, Johnston M, Zhang M (2012a) Automatic construction of invariant features using genetic programming for edge detection. In: Proceedings of the Australasian joint conference on artificial intelligence, pp 144–155
Fu W, Johnston M, Zhang M (2012b) Genetic programming for edge detection via balancing individual training images. In: Proceedings of the IEEE congress on evolutionary computation, pp 2597–2604
Fu W, Johnston M, Zhang M (2012c) Soft edge maps from edge detectors evolved by genetic programming. In: Proceedings of the IEEE congress on evolutionary computation, pp 24–31
Ganesan L, Bhattacharyya P (1997) Edge detection in untextured and textured images: a common computational framework. IEEE Trans Syst Man Cybern Part B Cybern 27(5):823–834
Gómez-Moreno H, Maldonado-Bascón S, López-Ferreras F (2001) Edge detection in noisy images using the support vector machines. In: Proceedings of the 6th international work-conference on artificial and natural neural networks: connectionist models of neurons, learning processes and artificial intelligence-part I, pp 685–692
Grigorescu C, Petkov N, Westenberg MA (2004) Contour and boundary detection improved by surround suppression of texture edges. Image Vision Comput 22(8):609–622
Hanley JA, McNeil BJ (1982) The meaning and use of the area under a receiver operating characteristic (ROC) curve. Radiology 143(1):29–36
Harris C, Buxton B (1996) Evolving edge detectors with genetic programming. In: Proceedings of the first annual conference on genetic programming, pp 309–314
Holm S (1979) A simple sequentially rejective multiple test procedure. Scand J Stat 6(2):65–70
Hong G, Asoke KN (2006) Breast cancer diagnosis using genetic programming generated feature. Pattern Recogn 39(5):980–987
Jiang JA, Chuang CL, Lu YL, Fahn CS (2007) Mathematical-morphology-based edge detectors for detection of thin edges in low-contrast regions. IEEE Trans Image Process 1(3):269–277
Kadar I, Ben-Shahar O, Sipper M (2009) Evolution of a local boundary detector for natural images via genetic programming and texture cues. In: Proceedings of the 11th annual conference on genetic and evolutionary computation, pp 1887–1888
Konishi S, Yuille A, Coughlan J, Zhu SC (2003) Statistical edge detection: learning and evaluating edge cues. IEEE Trans Pattern Anal Mach Intell 25(1):57–74
Kruskal WH (1957) Historical notes on the Wilcoxon unpaired two-sample test. J Am Stat Assoc 52(279):356–360
Lam L, Lee SW, Suen C (1992) Thinning methodologies-a comprehensive survey. IEEE Trans Pattern Anal Mach Intell 14(9):869–885
Lim DH, Jang SJ (2002) Comparison of two-sample tests for edge detection in noisy images. J Roy Stat Soc Ser D (The Statistician) 51(1):21–30
Lopez-Molina C, De Baets B, Bustince H (2013) Quantitative error measures for edge detection. Pattern Recogn 46(4):1125–1139
Mallat S (1987) A theory for multiresolution signal decomposition: the wavelet representation. IEEE Trans Pattern Anal Mach Intell 11(7):674–693
Marr D, Hildreth E (1980) Theory of edge detection. Proc Roy Soc Lond Ser B Biol Sci 207(1167):187–217
Martin D, Fowlkes C, Malik J (2004) Learning to detect natural image boundaries using local brightness, color, and texture cues. IEEE Trans Pattern Anal Mach Intell 26(5):530–549
Moreno R, Puig D, Julia C, Garcia M (2009) A new methodology for evaluation of edge detectors. In: Proceedings of the 16th IEEE international conference on image processing, pp 2157–2160
Neshatian K, Zhang M, Andreae P (2012) A filter approach to multiple feature construction for symbolic learning classifiers using genetic programming. IEEE Trans Evol Comput 16(5):645–661
Oppenheim A, Lim J (1981) The importance of phase in signals. Proc IEEE 69(5):529–541
Papari G, Petkov N (2011) Edge and line oriented contour detection: state of the art. Image Vision Comput 29:79–103
Papari G, Campisi P, Petkov N, Neri A (2007) A biologically motivated multiresolution approach to contour detection. EURASIP J Appl Sig Process 2007:1–28
Poli R (1996) Genetic programming for image analysis. In: Proceedings of the first annual conference on genetic programming, pp 363–368
Poli R, Langdon WB, McPhee NF (2008) A field guide to genetic programming. Published via http://www.lulu.com and freely available at http://www.gp-field-guide.org.uk, with contributions by J. R. Koza
Rezai-Rad G, Larijani HH (2007) A new investigation on edge detection filters operation for feature extraction under histogram equalization effect. In: Proceedings of the geometric modelling and imaging conference, pp 149–153
Schunck B (1987) Edge detection with Gaussian filters at multiple scales. In: Proceedings of the IEEE workshop on computer vision, representation and control, pp 208–210
Smart W, Zhang M (2005) Using genetic programming for multiclass classification by simultaneously solving component binary classification problems. In: Proceedings of the 8th European conference on genetic programming, pp 227–239
Spurrier JD (2003) On the null distribution of the Kruskal–Wallis statistic. J Nonparametric Stat 15(6):685–691
Tan X, Bhanu B, Lin Y (2005) Fingerprint classification based on learned features. IEEE Trans Syst Man Cybern Part C Appl Rev 35(3):287–300
Zhang M, Smart W (2006) Using Gaussian distribution to construct fitness functions in genetic programming for multiclass object classification. Pattern Recogn Lett 27(11):1266–1274
Zhang Y, Rockett PI (2005) Evolving optimal feature extraction using multi-objective genetic programming: a methodology and preliminary study on edge detection. In: Proceedings of the genetic and evolutionary computation conference, pp 795–802
Zhang Y, Li H, Niranjan M, Rockett P (2008) Applying cost-sensitive multiobjective genetic programming to feature extraction for spam e-mail filtering. In: Proceedings of the 11th European conference on genetic programming, pp 325–336
Author information
Authors and Affiliations
Corresponding author
Additional information
Communicated by V. Loia.
Rights and permissions
About this article
Cite this article
Fu, W., Johnston, M. & Zhang, M. Distribution-based invariant feature construction using genetic programming for edge detection. Soft Comput 19, 2371–2389 (2015). https://doi.org/10.1007/s00500-014-1432-4
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00500-014-1432-4