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2017 | OriginalPaper | Buchkapitel

A Multitree Genetic Programming Representation for Automatically Evolving Texture Image Descriptors

verfasst von : Harith Al-Sahaf, Bing Xue, Mengjie Zhang

Erschienen in: Simulated Evolution and Learning

Verlag: Springer International Publishing

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Abstract

Image descriptors are very important components in computer vision and pattern recognition that play critical roles in a wide range of applications. The main task of an image descriptor is to automatically detect micro-patterns in an image and generate a feature vector. A domain expert is often needed to undertake the process of developing an image descriptor. However, such an expert, in many cases, is difficult to find or expensive to employ. In this paper, a multitree genetic programming representation is adopted to automatically evolve image descriptors. Unlike existing hand-crafted image descriptors, the proposed method does not rely on predetermined features, instead, it automatically identifies a set of features using a few instances of each class. The performance of the proposed method is assessed using seven benchmark texture classification datasets and compared to seven state-of-the-art methods. The results show that the new method has significantly outperformed its counterpart methods in most cases.

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Vorheriges Kapitel Generalized Hybrid Evolutionary Algorithm Framework with a Mutation Operator Requiring no Adaptation

Nächstes Kapitel Running-Time Analysis of Particle Swarm Optimization with a Single Particle Based on Average Gain

Available at: http://multibandtexture.recherche.usherbrooke.ca.

Available at: http://www.outex.oulu.fi/index.php?page=classification.

Available at: http://www.cb.uu.se/~gustaf/KylbergSintornRotation/.

Available at: http://www.cb.uu.se/~gustaf/texture/.

Al-Sahaf, H., Al-Sahaf, A., Xue, B., Johnston, M., Zhang, M.: Automatically evolving rotation-invariant texture image descriptors by genetic programming. IEEE Trans. Evol. Comput. 21(1), 83–101 (2016)

Bhowan, U., Johnston, M., Zhang, M., Yao, X.: Reusing genetic programming for ensemble selection in classification of unbalanced data. IEEE Trans. Evol. Comput. 18(6), 893–908 (2014)CrossRef

Boric, N., Estevez, P.A.: Genetic programming-based clustering using an information theoretic fitness measure. In: Proceedings of 2007 IEEE Congress on Evolutionary Computation, pp. 31–38. IEEE (2007)

Brodatz, P.: Textures: A Photographic Album for Artists and Designers. Dover Publications, Mineola (1999)

Cha, S.-H.: Comprehensive survey on distance/similarity measures between probability density functions. Int. J. Math. Models Methods Appl. Sci. 1(4), 300–307 (2007)MathSciNet

Cordella, L.P., de Stefano, C., Fontanella, F., Marcelli, A.: Genetic programming for generating prototypes in classification problems. In: Proceedings of 2005 IEEE Congress on Evolutionary Computation, pp. 1149–1155. IEEE (2005)

Ebner, M., Zell, A.: Evolving a task specific image operator. In: Poli, R., Voigt, H.-M., Cagnoni, S., Corne, D., Smith, G.D., Fogarty, T.C. (eds.) EvoWorkshops 1999. LNCS, vol. 1596, pp. 74–89. Springer, Heidelberg (1999). doi:10.1007/10704703_6 CrossRef

Fu, W., Johnston, M., Zhang, M.: Distribution-based invariant feature construction using genetic programming for edge detection. Soft Comput. 19(8), 2371–2389 (2015)CrossRef

Guo, Z., Zhang, L., Zhang, D.: A completed modeling of local binary pattern operator for texture classification. IEEE Trans. Image Process. 19(6), 1657–1663 (2010)MathSciNetCrossRefMATH

10.

Koza, J.R.: Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge (1992)MATH

11.

Krig, S.: Computer Vision Metrics: Survey, Taxonomy, and Analysis, 1st edn. Apress, New York (2014)

12.

Kylberg, G.: The Kylberg texture dataset v. 1.0. External report (Blue series) 35, Centre for Image Analysis, Swedish University of Agricultural Sciences and Uppsala University, Uppsala, Sweden (2011)

13.

Kylberg, G.: Automatic virus identification using TEM: image segmentation and texture analysis. Ph.D. thesis, Division of Visual Information and Interaction, Uppsala University, Uppsala, Sweden (2014)

14.

Lee, J.-H., Ahn, C.W., An, J.: An approach to self-assembling swarm robots using multitree genetic programming. Sci. World J. 2013, 1–10 (2013)

15.

Mehta, R., Egiazarian, K.: Dominant rotated local binary patterns (DRLBP) for texture classification. Pattern Recogn. Lett. 71(1), 16–22 (2016)CrossRef

16.

Montana, D.J.: Strongly typed genetic programming. Evol. Comput. 3(2), 199–230 (1995)CrossRef

17.

Ojala, T., Mäenpää, T., Pietikäinen, M., Viertola, J., Kyllonen, J., Huovinen, S.: Outex - new framework for empirical evaluation of texture analysis algorithms. In: Proceedings of 16th International Conference on Pattern Recognition, vol. 1, pp. 701–706. IEEE (2002)

18.

Ojala, T., Pietikäinen, M., Mäenpää, T.: Gray scale and rotation invariant texture classification with local binary patterns. In: Vernon, D. (ed.) ECCV 2000. LNCS, vol. 1842, pp. 404–420. Springer, Heidelberg (2000). doi:10.1007/3-540-45054-8_27 CrossRef

19.

Olague, G., Trujillo, L.: A genetic programming approach to the design of interest point operators. In: Melin, P., Kacprzyk, J., Pedrycz, W. (eds.) Bio-inspired Hybrid Intelligent Systems for Image Analysis and Pattern Recognition. SCI, vol. 256, pp. 49–65. Springer, Heidelberg (2009). doi:10.1007/978-3-642-04516-5_3 CrossRef

20.

Poli, R., Langdon, W.B., McPhee, N.F.: A Field Guide to Genetic Programming (2008). Published via http://lulu.com. (With contributions by J.R. Koza)

21.

Willis, A., Sui, Y.: An algebraic model for fast corner detection. In: Proceedings of 12th IEEE International Conference on Computer Vision, pp. 2296–2302. IEEE (2009)

22.

Zhao, Y., Huang, D.-S., Jia, W.: Completed local binary count for rotation invariant texture classification. IEEE Trans. Image Process. 21(10), 4492–4497 (2012)MathSciNetCrossRef

Titel: A Multitree Genetic Programming Representation for Automatically Evolving Texture Image Descriptors
verfasst von: Harith Al-Sahaf
Bing Xue
Mengjie Zhang
Verlag: Springer International Publishing
Buch: Simulated Evolution and Learning
Print ISBN: 978-3-319-68758-2

Electronic ISBN: 978-3-319-68759-9

Copyright-Jahr: 2017
DOI: https://doi.org/10.1007/978-3-319-68759-9_41

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