Discovery of Texture Features Using Genetic Programming

Aus de_evolutionary_art_org
Wechseln zu: Navigation, Suche


Reference

Brian T. K. Lam: Discovery of Texture Features Using Genetic Programming. PhD Thesis, School of Computer Science and Information Technology, Science, Engineering, and Technology Portfolio, RMIT University, Melbourne, Victoria, Australia 30 March 2012.

DOI

Abstract

A visual texture is an image in which a basic pattern or texture element is repeated many times, for example grass in a lawn or bricks in a wall. Within each texture element, the grey levels and their positions are arranged in a sufficiently similar manner so that the patterns take on a uniform appearance. The process of characterising the underlying relationships within texture elements and their placement can be considered as a form of feature extrac- tion. These relationships allow salient features of different textures to be used in texture classification, segmentation and synthesis tasks. Texture classification is an important task in areas such as remote sensing, surface inspection, medical imaging and content retrieval from image databases.

Most texture feature extraction methods are derived from human intuition after much contemplation. Texture feature extraction remains a challenging problem due to the diversity and complexity of natural textures. In this thesis we investigate the evolution of feature extraction programs using tree based genetic programming.

Our main hypothesis is that given the right fitness evaluation, it may be possible to gen- erate new feature extraction programs independent of human intuition from basic properties of images such as pixel intensities, histograms and pixel positions. We used tree based genetic programming and a ‘learning set’ of thirteen Brodatz textures to evolve feature extraction programs. We have investigated three kinds of inputs/terminals: raw pixels, histograms andii a spatial encoding. The function set consisted of +, − to facilitate the analysis of the evolved programs. Fitness is computed with a novel application of clustering. A program in the pop- ulation is applied to a selection of images of two textures in the learning set. If the program delivers widely separated clusters for the two textures, it is considered to be very fit. The evolved programs were then used on a different training set of images to get a nearest neighbour classifier which is evaluated against a testing set. We have used the evolved feature extraction programs in 4 different classification tasks: (1) a thirteen class problem involving the same textures as in the learning set, but with an independent training and test set; (2) a four class problem comprising Brodatz textures not in the learning set; (3) a fifteen class problem comprising Vistex textures; and (4) a three class problem of malt classification. The evolved programs were evaluated by classification accuracy on the testing sets. Raw pixel input gave a classification accuracy of 50% for task 1 and 45% for task 3. Histogram input gave a classification accuracy of 81% and 75% for these tasks while the spatial en- coding gave accuracies of 75% and 61%. The histogram representation was found to be the most effective representation. The evolved programs were compared with 18 human derived methods on tasks 1 and 3. The accuracy of the evolved programs was ranked 14 out of 19 for task 1 and 9 out of 19 for task 3. Task 2 was only performed using histogram inputs and the accuracy was 100% compared with 95% for the grey level co-occurrence method. These results indicate that, on these tasks, the evolved feature extraction programs are competitive with human derived methods.

Task 4, malt classification, is a difficult real world problem. We used the best performing input, histograms, for this task. We obtained a classification accuracy of 67% which is better than the Gabor and Haar methods but worse than the gray level co-occurrence matrix and the grey level run length methods. However, when we combined the evolved features withiii human derived features, we improved the classification accuracy by 15%. This suggests that the evolved features have captured texture regularities not captured in the human derived methods. The contribution of the evolved features towards the improved accuracy was con- firmed when the combined evolved and human derived feature set was subjected to feature selection. There was a high percentage of evolved features among the selected features. The value of our approach lies in the fact that feature extraction programs can be evolved from simple inputs such as histograms and arithmetic operations without much domain knowledge. From a practitioner point of view, our set of programs has the advantage of not requiring the user to set the parameter values as required by many human derived methods. For researchers, our approach shows that it is possible to evolve, from simple inputs, feature extraction programs that can perform as well as those derived by human intuition.

Extended Abstract

Bibtex

Used References

[1] Davide Agnelli, Alessandro Bollini, and Luca Lombardi. Image classification: An evo- lutionary approach. Pattern Recognition Letters, 23(1-3):303–309, 2002.

[2] Aydn Akyol, Yusuf Yaslan, and Osman Erol. A genetic programming classifier design approach for cell images. In Khaled Mellouli, editor, Symbolic and Quantitative Ap- proaches to Reasoning with Uncertainty, volume 4724 of Lecture Notes in Computer Science, pages 878–888. Springer Berlin, Heidelberg, 2007.

[3] M. Amadasun and R. King. Textural features corresponding to textural proper- ties. IEEE Transactions on Systems, Man and Cybernetics, 19(5):1264–1274, Septem- ber/October 1989.

[4] David Andre. Automatically defined features: The simultaneous evolution of 2- dimensional feature detectors and an algorithm for using them. In Kenneth E. Kinnear, editor, Advances in Genetic Programming, chapter 23, pages 477–494. MIT Press, 1994.

[5] David Andre. Learning and upgrading rules for an OCR system using genetic program- ming. In Proceedings of the 1994 IEEE World Congress on Computational Intelligence, volume 1, pages 462–467, Orlando, Florida, USA, 27-29 June 1994. IEEE Press.

[6] Peter J. Angeline. An investigation into the sensitivity of genetic programming to the frequency of leaf selection during subtree crossover. In John R. Koza, David E. Goldberg, David B. Fogel, and Rick L. Riolo, editors, Genetic Programming 1996: Proceedings of the First Annual Conference, pages 21–29, Stanford University, CA, USA, 28–31 July 1996. MIT Press.

[7] Shinya Aoki and Tomoharu Nagao. Automatic construction of tree-structural image transformations using genetic programming. In ICIAP ’99: Proceedings of the 10th International Conference on Image Analysis and Processing, page 136, Washington, DC, USA, 1999. IEEE Computer Society.

[8] S. Arivazhagan, L. Ganesan, and T. G. Subash Kumar. Texture classification using ridgelet transform. Pattern Recognition Letters, 27(16):1875–1883, December 2006.

[9] Melanie Aurnhammer. Evolving texture features by genetic programming. In Mario Gi- acobini et al., editor, Applications of Evolutionary Computing: EvoWorkshops 2007: EvoCOMNET, EvoFIN, EvoIASP, EvoINTERACTION, EvoMUSART, EvoSTOC, and EvoTransLog, pages 351–358, Valencia, 2007.

[10] Engin Avci, Abdulkadir Sengur, and Davut Hanbay. An optimum feature extraction method for texture classification. Expert Systems with Applications, 36(3, Part 2):6036 – 6043, 2009.

[11] J.F. Baldwin, T.P. Martin, and J.G. Shanahan. Automatic fuzzy Cartesian granule feature discovery using genetic programming in image understanding. In Fuzzy Systems Proceedings, 1998. IEEE World Congress on Computational Intelligence, volume 2, pages 1631–1636, May. 1998.

[12] Wolfgang Banzhaf, Peter Nordin, Robert E. Keller, and Frank D. Francone. Genetic Programming – An Introduction: On the Automatic Evolution of Computer Programs and its Applications. Morgan Kaufmann, San Francisco, CA, USA, January 1998.

[13] Tony Belpaeme. Evolution of visual feature detectors. In Riccardo Poli, Stefano Cagnoni, Hans-Michael Voigt, Terry Fogarty, and Peter Nordin, editors, Late Breaking Papers at EvoISAP’99: The First European Workshop on Evolutionary Computation in Image Analysis and Signal Processing, pages 1–10, Goteborg, Sweden, 28 May 1999.

[14] Karl Benson. Performing classification with an environment manipulating mutable au- tomata. In Proceedings of the 2000 Congress on Evolutionary Computation CEC2000, volume 1, pages 264–271, La Jolla Marriott Hotel La Jolla, California, USA, 6-9 July 2000. IEEE Press.

[15] Bir Bhanu, Jiangang Yu, Xuejun Tan, and Yingqiang Lin. Feature synthesis using genetic programming for face expression recognition. In Deb at al., editor, Genetic and Evolutionary Computation – GECCO-2004, Part II, volume 3103 of Lecture Notes in Computer Science, pages 896–907, Seattle, WA, USA, 26-30 June 2004. Springer- Verlag.

[16] Tobias Blickle and Lothar Thiele. A comparison of selection schemes used in genetic algorithms. Technical Report 11, Swiss Federal Institute of Technology (ETH), Glori- astrasse 35, 8092 Zurich, Switzerland, 1995.

[17] P. Brodatz. Textures: A Photographic Album for Artists and Designers. Dover, New York, 1966.

[18] F.W. Campbell and J.G. Robson. An application of Fourier analysis to the visibility of contrast gratings. Journal of Physiology, 197(3):551–566, 1968.

[19] Y.Q. Chen. Novel Techniques for Image Texture Classification. PhD thesis, School of Electronics and Computer Science, University of South Hampton, 1995.

[20] Zheng Chen and Siwei Lu. A genetic programming approach for classification of tex- tures based on wavelet analysis. In IEEE International Symposium on Intelligent Signal Processing, WISP 2007, pages 1–6, October 2007.

[21] Clement Chion, Jacques-Andre Landry, and Luis Da Costa. A genetic-programming- based method for hyperspectral data information extraction: Agricultural applications. IEEE Transactions on Geoscience and Remote Sensing, 46(8):2446–2457, August 2008.

[22] Karen E. Churchill. Structure-function Relationships in Barley and Malt Starch: In- fluence on Malting Quality. PhD thesis, Department of Food Science and Nutrition, University of Minnesota, 1999.

[23] R.W. Conners and C.A. Harlow. A theoretical comparison of texture algorithms. In IEEE Transactions on Pattern Analysis and Machine Intelligence, volume PAMI-2, pages 204–222, May 1980.

[24] Jason M. Daida, Jonathan D. Hommes, Tommaso F. Bersano-Begey, Steven J. Ross, and John F. Vesecky. Algorithm discovery using the genetic programming paradigm: Extracting low-contrast curvilinear features from SAR images of Arctic ice. In Pe- ter J. Angeline and K. E. Kinnear, Jr., editors, Advances in Genetic Programming 2, chapter 21, pages 417–442. MIT Press, Cambridge, MA, USA, 1996.

[25] Z. Dapeng and L. Zhongrong. Digital image texture analysis using gray level and energy cooccurrence. Proceedings of International Society for Optical Engineering Conference on Applications of Artificial Intelligence IV, 657(5):152–156, April 1986.

[26] Manoranjan Dash and Huan Liu. Consistency based feature selection. Artificial Intel- ligence, 151(1-2):155–176, 2003.

[27] Ivanoe De Falco, Antonio Della Cioppa, and Ernesto Tarantino. Unsupervised spectral pattern recognition for multispectral images by means of a genetic programming app- roach. In David B. Fogel, Mohamed A. El-Sharkawi, Xin Yao, Garry Greenwood, Hi- toshi Iba, Paul Marrow, and Mark Shackleton, editors, Proceedings of the 2002 Congress on Evolutionary Computation (CEC2002), pages 231–236. IEEE Press, 2002.

[28] C. De Stefano, A. Della Cioppa, and A. Marcelli. Character preclassification based on genetic programming. Pattern Recognition Letters, 23(12):1439–1448, 2002.

[29] L.F. Garcia del Moral, A. Sopena, J.L. Montoya, P. Polo, J. Voltas, and P. Codesal. Image analysis of grain and chemical composition of the barley plant as predictors of malting quality in Mediterranean environments. Cereal Chemistry, 75(5):755–761, 1998.

[30] Patrik D’Haeseleer. Context preserving crossover in genetic programming. In Proceed- ings of the 1994 IEEE World Congress on Computational Intelligence, volume 1, pages 256–261, Orlando, Florida, USA, 27-29 June 1994. IEEE Press.

[31] Ondej Drbohlav and Ale Leonardis. Towards correct and informative evaluation methodology for texture classification under varying viewpoint and illumination. Com- puter Vision and Image Understanding, 114(4):439 – 449, 2010.

[32] Marc Ebner. On the evolution of edge detectors for robot vision using genetic pro- gramming. In Horst-Michael Groß, editor, Workshop SOAVE ’97 - Selbstorganisation von Adaptivem Verhalten, VDI Reihe 8 Nr. 663, pages 127–134, D ̈usseldorf, 1997. VDIVerlag.

[33] A. E. Eiben and Mark Jelasity. A critical note on experimental research methodology in EC. In Proceedings of The 2002 Congress on Evolutionary Computation (CEC2002), pages 582–587. IEEE, 2002.

[34] Andries P. Engelbrecht. Computational Intelligence: An Introduction. Wiley, 2007.

[35] David B. Fogel and Lawrence J. Fogel. An introduction to evolutionary programming. In AE ’95: Selected Papers from the European conference on Artificial Evolution, pages 21–33, London, UK, 1996. Springer-Verlag.

[36] I. Fogel and D. Sagi. Gabor filters as texture discriminator. Biological Cybernetics, 61(2):121–144, 1989.

[37] S. R. Fountain, T. N. Tan, and K. D. Baker. A comparative study of rotation invariant classification and retrieval of texture images. In Proceedings of British Machine Vision Conference, pages 266–275, 1998.

[38] M. D. Fox, C. C. Chen, and J. S. Daponte. Fractal feature analysis and classification in medical imaging. IEEE Transactions on Medical Imaging, 8(2):133–142, June 1989.

[39] Christian Gagn ́e, Marc Schoenauer, Marc Parizeau, and Marco Tomassini. Genetic pro- gramming, validation sets, and parsimony pressure. In Pierre Collet, Marco Tomassini, Marc Ebner, Steven Gustafson, and Anik ́o Ek ́art, editors, Proceedings of the 9th Euro- pean Conference on Genetic Programming, volume 3905 of Lecture Notes in Computer Science, pages 109–120, Budapest, Hungary, 10-12 April 2006. Springer.

[40] M. M. Galloway. Textural analysis using gray level run lengths. Computer Vision, Graphics, and Image Processing, 4(2):172–179, 1975.

[41] M. A. Georgeson. Spatial frequency analysis and human vision. Tutorial Essays in Psychology, 2, 1979. N. S. Sutherland, L. Erlbaum Associates, Hillsdale N.J.

[42] J.J. Gibson. The Perception of the Visual World. Houghton Mifflin, 1950.

[43] L. Van Gool, P. Dewaele, and A. Oosterlinck. Texture analysis anno 1983. Computer Vision, Graphics, and Image Processing, 29(3):336–357, 1985.

[44] Helen F. Gray, Ross J. Maxwell, Irene Martinez-Perez, Carles Arus, and Sebastian Cerdan. Genetic programming for classification and feature selection: Analysis of 1H nuclear magnetic resonance spectra from human brain tumour biopsies. Nuclear Magnetic Resonance Biomedicine, 11(4-5):217–224, June-August 1998.

[45] Frederic Gruau. On using syntactic constraints with genetic programming. In Pe- ter J. Angeline and K. E. Kinnear, Jr., editors, Advances in Genetic Programming 2, chapter 19, pages 377–394. MIT Press, Cambridge, MA, USA, 1996.

[46] A. Guarda, C. Le Gal, and A. Lux. Evolving visual features and detectors. In Proceed- ings of the International Symposium on Computer Graphics, Image Processing, and Vision, SIBGRAPI’98, pages 246–253, Washington, DC, USA, 1998. IEEE Computer Society.

[47] Hong Guo and Asoke K. Nandi. Breast cancer diagnosis using genetic programming generated feature. Pattern Recognition, 39(5):980–987, 2006.

[48] Pei-Fang Guo, Prabir Bhattacharya, and Nawwaf Kharma. An efficient image pattern recognition system using an evolutionary search strategy. In IEEE International Con- ference on Systems, Man and Cybernetics, (SMC 2009), pages 599–604, San Antonio, Texas, USA, October 2009. IEEE.

[49] E. Hadjidemetriou, M.D. Grossberg, and S.K. Nayar. Spatial information in multires- olution histograms. In Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (CVPR 2001), volume 1, pages 702–709, 2001.

[50] R.M. Haralick. Statistical and structural approaches to texture. In Proceedings of the IEEE, volume 67, pages 786–804, May 1979.

[51] Robert M. Haralick, K. Shanmugam, and Itshak Distein. Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, 3(6):610–621, 11 1973.

[52] Christopher Harris and Bernard Buxton. Evolving edge detectors with genetic pro- gramming. In John R. Koza, David E. Goldberg, David B. Fogel, and Rick L. Riolo, editors, Proceedings of the First Annual Conference on Genetic Programming 1996, pages 309–315, Stanford University, CA, USA, 28–31 July 1996. MIT Press

[53] N.R. Harvey, J. Theiler, S.P. Brumby, S. Perkins, J.J. Szymanski, J.J. Bloch, R.B. Porter, M. Galassi, and A.C. Young. Comparison of GENIE and conventional su- pervised classifiers for multispectral image feature extraction. IEEE Transactions on Geoscience and Remote Sensing, 40(2):393–404, February 2002.

[54] Eric Hayman, Barbara Caputo, Mario Fritz, and Jan olof Eklundh. On the significance of real-world conditions for material classification. In Tomas Pajdla and Jiri Mats, editors, Proceedings of European Conference on Computer Vision 2004, volume 4, pages 253–266. Springer, 2004.

[55] Javier Hervas and Paul L. Rosin. Image thresholding for landslide detection by ge- netic programming. In Lorenzo Bruzzone and Paul Smits, editors, Proceedings of the First International Workshop on Multitemporal Remote Sensing Images, pages 65–72, University of Trento, Italy, 13-14 September 2002. World Scientific Publishing.

[56] John H. Holland. Genetic algorithms and the optimal allocation of trials. Society for Industrial and Applied Mathematics Journal on Computing, 2(2):88–105, 1973.

[57] Gordon S. Hollingworth, Steve L. Smith, and Andy M. Tyrrell. Design of highly parallel edge detection nodes using evolutionary techniques. In Proceedings of the Seventh Euromicro Workshop on Parallel and Distributed Processing, PDP ’99, pages 35–42, Funchal, 3-5 February 1999. IEEE.

[58] Robert C. Holte. Very simple classification rules perform well on most commonly used datasets. Machine Learning, 11(1):63–91, 1993.

[59] Michael De Hoon. Cluster 3.0. http://bonsai.hgc.jp/∼mdehoon/software/software.html, 2002. Visited : 01/01/2004.

[60] D.C. Hope, E. Munday, and S.L. Smith. Evolutionary algorithms in the classification of mammograms. In IEEE Symposium on Computational Intelligence in Image and Signal Processing, 2007. (CIISP 2007), pages 258–265. IEEE, April 2007.

[61] Daniel Howard, Simon C. Roberts, and Conor Ryan. Pragmatic genetic program- ming strategy for the problem of vehicle detection in airborne reconnaissance. Pattern Recognition Letters, 27(11):1275–1288, 2006.

[62] Hitoshi Iba, Taisuke Sato, and Hugo de Garis. Recombination guidance for numerical genetic programming. In 1995 IEEE Conference on Evolutionary Computation, vol- ume 1, pages 97–102, Perth, Australia, 29 November - 1 December 1995. IEEE Press.

[63] Bernd Jahne. Introduction. In Bernd Jahne, Hoirst Haussecker, and Peter Geissler, editors, Handbook of Computer Vision and Applications, volume 2, chapter 1, pages 1–6. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1999.

[64] A. K. Jain, D. H. Alman, and F. Farrokhnia. Texture analysis of automotive finishes. In Proceedings of Society of Manufacturing Engineers Machine Vision Applications Conference, pages 1–16. IEEE Press, 1990. Detroit, USA.

[65] A. K. Jain, S. K. Bhattacharjee, and Y. Chen. On texture in document images. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 677–680, Champaign, Il, USA, 1992. IEEE Press.

[66] Michael Patrick Johnson, Pattie Maes, and Trevor Darrell. Evolving visual routines. In Rodney A. Brooks and Pattie Maes, editors, Artificial Life IV, Proceedings of the fourth International Workshop on the Synthesis and Simulation of Living Systems, pages 198–209, MIT, Cambridge, MA, USA, 6-8 July 1994. MIT Press.

[67] I. Kadar, O. Ben-Shahar, and M. Sipper. Evolving boundary detectors for natural images via genetic programming. In 19th International Conference on Pattern Recog- nition, 2008. (ICPR 2008), pages 1–4. IEEE Press, December 2008.

[68] Umasankar Kandaswamy, S. A. Schuckers, and Donald A. Adjeroh. Comparison of texture analysis schemes under nonideal conditions. IEEE Transactions on Image Processing, 20(8):2260–2275, 2011.

[69] R.L. Kashyap, R. Chellappa, and A. Khotanzad. Texture classification using features derived from random field models. Pattern Recognition Letters, 1(1):43–50, 1982.

[70] K. M. Khayrul Bashar and N. Ohnishi. Fusing cortex transform and intensity based features for image texture classification. In Proceedings of the Fifth International Con- ference on Information Fusion, 2002, volume 2, pages 1463–1469. International Society of Information Fusion Press, July 2002.

[71] Jr. Kinnear, K.E. Fitness landscapes and difficulty in genetic programming. In Pro- ceedings of the First IEEE Conference on Evolutionary Computation, IEEE World Congress on Computational Intelligence, 1994., volume 1, pages 142–147, June 1994.

[72] Kenneth E. Kinnear, Jr. Fitness landscapes and difficulty in genetic programming. In Proceedings of the 1994 IEEE World Conference on Computational Intelligence, volume 1, pages 142–147, Orlando, Florida, USA, 27-29 June 1994. IEEE Press.

[73] Kenji Kira and Larry A. Rendell. The Feature Selection Problem: Traditional Methods and a New Algorithm. In Proceedings of The Tenth National Conference on Artificial Intelligence, pages 129–134, Cambridge, MA, USA, 1992. AAAI Press and MIT Press.

[74] Russell A. Kirsch. Computer determination of the constituent structure of biological images. Computers and Biomedical Research, 4(3):315 – 328, 1971.

[75] Mario Koppen and Bertram Nickolay. Genetic programming based texture filtering framework. Pattern Recognition in Soft Computing Paradigm, pages 275–304, 2001. World Scientific Publishing Co., Inc.

[76] Taras Kowaliw, Wolfgang Banzhaf, Nawwaf Kharma, and Simon Harding. Evolving novel image features using genetic programming-based image transforms. In Andy Tyrrell, editor, 2009 IEEE Congress on Evolutionary Computation, pages 2502–2507, Trondheim, Norway, 18-21 May 2009. IEEE Press.

[77] John R. Koza. Human-competitive results produced by genetic programming. http://www.genetic-programming.com/humancompetitive.html. Visited: 01/01/2011.

[78] John R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, Cambridge, MA, USA, 1992.

[79] John R. Koza. The genetic programming paradigm: Genetically breeding populations of computer programs to solve problems. In Branko Soucek and the IRIS Group, editors, Dynamic, Genetic, and Chaotic Programming, pages 203–321. John Wiley, New York, 1992.

[80] Krzysztof Krawiec. Evolutionary learning of primitive-based visual concepts. In Gary G. Yen, Lipo Wang, Piero Bonissone, and Simon M. Lucas, editors, IEEE Congress on Evolutionary Computation (CEC 2006), pages 1308 –1315, July 2006.

[81] Krzysztof Krawiec and Bir Bhanu. Visual learning by evolutionary and coevolutionary feature synthesis. IEEE Transactions on Evolutionary Computation, 11(5):635–650, October 2007.

[82] Krzysztof Krawiec, Daniel Howard, and Mengjie Zhang. Overview of object detection and image analysis by means of genetic programming techniques. In Proceedings of the 2007 International Conference on Frontiers in the Convergence of Bioscience and Information Technologies (FBIT 2007), pages 779–784, Jeju Island, Korea, October 11-13 2007. IEEE Press.

[83] Krishna K. Kshitiz, Shahab Sokhansanj, Hugh C. Wood, and Philip W. Winter. Mea- surements of malt attributes by machine vision. Proceedings of The International So- ciety for Optics and Photonics, 3543(152):152–163, 1998.

[84] Ibrahim Kushchu. An evaluation of evolutionary generalisation in genetic programming. Artificial Intelligence Review, 18(1):3–14, September 2002.

[85] MIT Media Lab. The Vistex Image Database, MIT. http://vismod.media.mit.edu/vismod/imagery/VisionTexture/. Visited:01/01/2011.

[86] A. Laine and J. Fan. Texture classification by wavelet packet signatures. IEEE Trans- actions on Pattern Analysis and Machine Intelligence, 15(11):1186 –1191, November 1993.

[87] Brian Lam and Vic Ciesielski. Discovery of human-competitive image texture feature extraction programs using genetic programming. In Kalyanmoy Deb, Riccardo Poli, Wolfgang Banzhaf, Hans-Georg Beyer, Edmund Burke, Paul Darwen, Dipankar Das- gupta, Dario Floreano, James Foster, Mark Harman, Owen Holland, Pier Luca Lanzi, Lee Spector, Andrea Tettamanzi, Dirk Thierens, and Andy Tyrrell, editors, Genetic and Evolutionary Computation – GECCO-2004, Part II, volume 3103 of Lecture Notes in Computer Science, pages 1114–1125, Seattle, WA, USA, 26-30 June 2004. Springer- Verlag.

[88] Meng Lamei, Li Yamin, and Zhu Huanrong. Method of plant texture image recognition based on genetic programming. In 2010 International Conference on Computer Design and Applications (ICCDA), volume 1, pages 370–373. IEEE Press, June 2010.

[89] W. B. Langdon. The evolution of size in variable length representations. In 1998 IEEE World Congress on Computational Intelligence, pages 633–638, Anchorage, Alaska, USA, 5-9 May 1998. IEEE Press.

[90] W. B. Langdon. Size fair and homologous tree crossovers for tree genetic programming. Genetic Programming and Evolvable Machines, 1:95–119, April 2000.

[91] W. B. Langdon and J. P. Nordin. Seeding GP populations. In Riccardo Poli, Wolfgang Banzhaf, William B. Langdon, Julian F. Miller, Peter Nordin, and Terence C. Fogarty, editors, Proceedings of European Conference on Genetic Programming (EuroGP’2000), volume 1802 of LNCS, pages 304–315, Edinburgh, 15-16 April 2000. Springer-Verlag.

[92] W. B. Langdon and Riccardo Poli. Foundations of Genetic Programming. Springer- Verlag, 2002.

[93] William B. Langdon. Size fair and homologous tree genetic programming crossovers. Genetic Programming and Evolvable Machines, 1(1/2):95–119, April 2000.

[94] William B. Langdon and John R. Koza. Genetic Programming and Data Structures: Genetic Programming + Data Structures = Automatic Programming! Kluwer Aca- demic Publishers, Norwell, MA, USA, 1998.

[95] William B. Langdon, Tery Soule, Riccardo Poli, and James A. Foster. The evolution of size and shape, pages 163–190. MIT Press, Cambridge, MA, USA, 1999.

[96] K. Laws. Texture Image Segmentation. PhD thesis, University of Southern California, Department of Engineering, Los Angeles, 1980.

[97] Thomas Leung and Jitendra Malik. Representing and recognizing the visual appear- ance of materials using three-dimensional textons. International Journal of Computer Vision, 43(1):29–44, 2001.

[98] L. Li, C. S. Tong, and S. K. Choy. Texture classification using refined histogram. IEEE Transactions on Image Processing, 19(5):1371–1378, May 2010. IEEE Press.

[99] Rui Li, Bir Bhanu, and Anlei Dong. Coevolutionary feature synthesized EM algor- ithm for image retrieval. In MULTIMEDIA ’05: Proceedings of the 13th annual ACM International Conference on Multimedia, pages 696–705, New York, NY, USA, 2005. ACM.

[100] Rui Li, Bir Bhanu, and Anlei Dong. Feature synthesized EM algorithm for image retrieval. ACM Transaction on Multimedia Computing, Communications and Applica- tions, 4(2):1–24, 2008.

[101] Rui Li, Bir Bhanu, and Krzysztof Krawiec. Hybrid coevolutionary algorithms vs. SVM algorithms. In GECCO ’07: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, pages 456–463, New York, NY, USA, 2007. ACM.

[102] Shutao Li, Yi Li, and Yaonan Wang. Feature selection for high-dimensional data: A fast correlation-based filter solution. In Tom Fawcett and Nina Mishra, editors, Proceedings of the 20th International Conference on Machine Learning, pages 56–63, August 2003. Association for the Advancement of Artificial Intelligence (AAAI).

[103] Shutao Li, Yi Li, and Yaonan Wang. Combining wavelet and ridgelet transforms for texture classifications using support vector machines. In Proceedings of 2004 Interna- tional Symposium on Intelligent Multimedia, Video and Speech Processing, 2004, pages 442–445. IEEE, October 2004.

[104] Stan Z. Li, Kap Luk Chan, and Changliang Wang. Performance evaluation of the nearest feature line method in image classification and retrieval. IEEE Transactions on Pattern Analysis and Machine Intelligence, 22(11):1335–1349, November 2000.

[105] Yamin Li, Jinru Ma, and Qiuxia Zhao. Two improvements in genetic programming for image classification. In Jun Wang, editor, 2008 IEEE World Congress on Computa- tional Intelligence, pages 2492 –2497, Hong Kong, 1-6 June 2008. IEEE Press.

[106] Tjen-Sien Lim, Wei-Yin Loh, and Yu-Shan Shih. A comparison of prediction accuracy, complexity, and training time of thirty-three old and new classification algorithms. Machine Learning, 40(3):203–228, 2000.

[107] Yingqiang Lin and B. Bhanu. Evolutionary feature synthesis for object recognition. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Re- views, 35(2):156 –171, May 2005.

[108] Yingqiang Lin and Bir Bhanu. Learning features for object recognition. In Erick Cantu- Paz et al., editor, Proceedings of the 2003 International Conference on Genetic and Evolutionary Computation: PartII, (GECCO’03), pages 2227–2239, Berlin, Heidelberg, 2003. Springer-Verlag.

[109] Bradley J. Lucier, Sudhakar Mamillapalli, and Jens Palsberg. Program optimization for faster genetic programming. In John R. Koza, Wolfgang Banzhaf, Kumar Chellapilla, Kalyanmoy Deb, Marco Dorigo, David B. Fogel, Max H. Garzon, David E. Goldberg, Hitoshi Iba, and Rick Riolo, editors, Proceedings of the Third Genetic Programming Annual Conference, pages 202–207, University of Wisconsin, Madison, Wisconsin, USA, 22-25 July 1998. Morgan Kaufmann.

[110] George F. Luger. Artificial Intelligence: Structures and Strategies for Complex Problem Solving. Addison-Wesley Longman Publishing Co., Inc., Boston, MA, USA, 1997.

[111] Hela Mahersia and Kamel Hamrouni. New rotation invariant features for texture classi- fication. In A. H. M. Zahirul Alam et al., editor, International Conference on Computer and Communication Engineering, 2008. ICCCE 2008, pages 687–690, May 2008. In- ternational Islamic University of Malaysia.

[112] Hela Mahersia and Kamel Hamrouni. Rotation and scale-invariant texture classification using log-polar and ridgelet transforms. Machine Graphics and Vision, 18(2):215–232, January 2009. Polish Academy of Sciences.

[113] S. Majumdar and D. S. Jayas. Classification of bulk samples of cereal grains using machine vision. Journal of Agriculture Engineering Research, 73(1):35–47, 1999.

[114] Benoit B. Mandelbrot. The Fractal Geometry of Nature. W. H. Freeman, 1st edition, August 1982.

[115] Jianchang Mao and Anil K. Jain. Texture classification and segmentation using mul- tiresolution simultaneous autoregressive models. Pattern Recognition, 25(2):173–188, 1992.

[116] A. Marcelli. Exploring genetic programming for modeling character shape. In 2000 IEEE International Conference on Systems, Man, and Cybernetics, volume 4, pages 2757–2762. IEEE Press, 2000.

[117] Matteo Masotti and Renato Campanini. Texture classification using invariant ranklet features. Pattern Recognition Letters, 29(14):1980–1986, October 2008.

[118] S. R. Maxwell. Why might some problems be difficult for genetic programming to find solutions? In John R. Koza et al., editor, Late Breaking Papers at the Genetic Programming 1996 Conference, pages 125–128, Stanford University, CA, USA, 28–31 July 1996. Stanford University Press.

[119] Ben McKay, Mark J. Willis, and Geoffrey W. Barton. Using a tree structured genetic algorithm to perform symbolic regression. In A. M. S. Zalzala, editor, First Inter- national Conference on Genetic Algorithms in Engineering Systems: Innovations and Applications, (GALESIA), pages 487–492, Sheffield, UK, 12-14 September 1995. IEEE Press.

[120] Robert I. Mckay, Nguyen Xuan Hoai, Peter Alexander Whigham, Yin Shan, and Michael O’Neill. Grammar-based genetic programming: A survey. Genetic Program- ming and Evolvable Machines, 11(3-4):365–396, September 2010.

[121] Jaime Melendez, Domenec Puig, and Miguel Angel Garcia. Multi-level pixel-based texture classification through efficient prototype selection via normalized cut. Pattern Recognition, 43(12):4113–4123, December 2010.

[122] Julian F. Miller, Stephen L. Smith, and Yuan Zhang. Detection of microcalcifications in mammograms using multi-chromosome Cartesian genetic programming. In Jur- gen Branke et al., editor, GECCO ’10: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, pages 1923–1930, New York, NY, USA, 2010. ACM.

[123] M. Mirmehdi, X.H. Xie, and J. Suri. Handbook of Texture Analysis. Imperial College Press, London, 2008.

[124] M. Moll. An update of analytical procedures for the determination of malt modification and malt homogeneity, part 1. Monatsschrift fur Brauwissenschaft, 3(4):92–97, 1996.

[125] M. Moll. An update of analytical procedures for the determination of malt modification and malt homogeneity, part 2. Monatsschrift fur Brauwissenschaft, 5(6):171–177, 1996.

[126] M. Moll. An update of analytical procedures for the determination of malt modification and malt homogeneity, part 3. Monatsschrift fur Brauwissenschaft, 9(10):283–296, 1996.

[127] M. Moll. An update of analytical procedures for the determination of malt modification and malt homogeneity, part 4. Monatsschrift fur Brauwissenschaft, 1(2):12–16, 1997.

[128] David J. Montana. Strongly typed genetic programming. Evolutionary Computation, 3(2):199–230, 1995.

[129] R. J. Nandi, A. K. Nandi, R. Rangayyan, and D. Scutt. Genetic programming and feature selection for classification of breast masses in mammograms. In the 28th Annual International Conference of the Engineering in Medicine and Biology Society, (EMBS ’06), pages 3021–3024, New York, USA, August 2006. IEEE Press.

[130] Nikolay Nikolaev and Hitoshi Iba. Adaptive Learning of Polynomial Networks. Num- ber 4 in Genetic and Evolutionary Computation series. Springer, June 2006.

[131] Seishi Ninomiya, Akihiro Sasaki, and Khoichi Takemura. Evaluation of fineness of wrinkles on husks of malting barley Hordeum vulgare L. by texture analysis of digital image data. Euphytica, 64:113–121, 1992.

[132] P.P. Ohanian and R.C. Dubes. Performance evaluation for four classes of texture features. Pattern Recognition, 25(8):819–833, 1992.

[133] Timo Ojala, Matti Pietik ̈ainen, and Topi M ̈aenp ̈aa ̈. Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Transactions on Pattern Analysis and Machine Intelligence, 24(7):971–987, July 2002.

[134] Gustavo Olague, Eva Romero, Leonardo Trujillo, and Bir Bhanu. Multiclass ob- ject recognition based on texture linear genetic programming. In Proceedings of the 2007 EvoWorkshops 2007 on EvoCoMnet, EvoFIN, EvoIASP,EvoINTERACTION, EvoMUSART, EvoSTOC and EvoTransLog: Applications of Evolutionary Computing, pages 291–300, Berlin, Heidelberg, 2007. Springer-Verlag.

[135] Wumo Pan, T. D. Bui, and C. Y. Suen. Rotation invariant texture classification by ridgelet transform and frequency-orientation space decomposition. Signal Processing, 88(1):189–199, January 2008.

[136] Schmuel Peleg, Joseph Naor, Ralph Hartley, and David Avnir. A comparison of clus- tering algorithms applied to color image quantization. IEEE Transactions on Pattern Analysis and Machine Intelligence, 6(4):518–523, 1984.

[137] Maria Petrou and Pedro Garcia Sevilla. Image Processing - Dealing With Texture. John Wiley & Sons, Ltd, 2006.

[138] Arie Pikaz and Amir Averbuch. An efficient topological characterization of gray-level textures, using a multiresolution representation. Graphical Models and Image Process- ing, 59(1):1–17, 1997.

[139] Brian Pinto and Andy Song. Motion detection in complex environments by genetic programming. In GECCO ’09: Proceedings of the 11th Annual Conference Companion on Genetic and Evolutionary Computation Conference, pages 2125–2130, New York, NY, USA, 2009. ACM.

[140] Riccardo Poli. Genetic programming for feature detection and image segmentation. In T. C. Fogarty, editor, Evolutionary Computing, number 1143 in Lecture Notes in Computer Science, pages 110–125. Springer-Verlag, University of Sussex, UK, 1-2 April 1996.

[141] Riccardo Poli. Genetic programming for image analysis. In John R. Koza, David E. Goldberg, David B. Fogel, and Rick L. Riolo, editors, Genetic Programming 1996: Proceedings of the First Annual Conference, pages 363–368, Stanford University, CA, USA, 28–31 July 1996. MIT Press.

[142] Riccardo Poli and Stefano Cagnoni. Genetic programming with user-driven selection: Experiments on the evolution of algorithms for image enhancement. In John R. Koza, Kalyanmoy Deb, Marco Dorigo, David B. Fogel, Max Garzon, Hitoshi Iba, and Rick L. Riolo, editors, Genetic Programming 1997: Proceedings of the Second Annual Confer- ence, pages 269–277, Stanford University, CA, USA, 13-16 July 1997. Morgan Kauf- mann.

[143] Riccardo Poli and William B. Langdon. On the ability to search the space of programs of standard, one-point and uniform crossover in genetic programming. Technical Report CSRP-98-7, University of Birmingham, School of Computer Science, January 1998.

[144] Riccardo Poli and Nicholas Freitag McPhee. Exact schema theorems for GP with one-point and standard crossover operating on linear structures and their application to the study of the evolution of size. Technical Report CSRP-00-14, University of Birmingham, School of Computer Science, October 2000.

[145] Riccardo Poli and Nicholas Freitag McPhee. General schema theory for genetic programming with subtree-swapping crossover: Part I. Evolutionary Computation, 11(1):53–66, March 2003.

[146] Riccardo Poli and Nicholas Freitag McPhee. General schema theory for genetic programming with subtree-swapping crossover: Part II. Evolutionary Computation, 11(2):169–206, June 2003.

[147] Cesar Puente, Gustavo Olague, Stephen V. Smith, Stephen Bullock, Miguel A. Gon- zalez, and Alejandro Hinojosa. Genetic programming methodology that synthesize vegetation indices for the estimation of soil cover. In Gunther Raidl et al., editor, GECCO ’09: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, pages 1593–1600, New York, NY, USA, 2009. ACM.

[148] Marcos I. Quintana, Riccardo Poli, and Ela Claridge. Morphological algorithm design for binary images using genetic programming. Genetic Programming and Evolvable Machines, 7(1):81–102, March 2006.

[149] K.M. Rajpoot and N.M. Rajpoot. Wavelets and support vector machines for texture classification. In Proceedings of the 8th International Multitopic Conference (INMIC 2004), pages 328–333. IEEE Press, December 2004.

[150] Trygve Randen and John Hakon Husoy. Filtering for texture classification: A com- parative study. IEEE Transactions on Pattern Analysis and Machine Intelligence, 21(4):291–310, April 1999.

[151] A.R. Rao. A Taxonomy for Texture Description and Identification. Springer-Verlag New York, Inc., New York, NY, USA, 1990.

[152] P. Reinikainen, J. Hirvonen, N. Jaakkola, and J. Olkku. Image processing of halved kernels in the control of malting and malt quality. Journal of the American Society of Brewing Chemists, 54(1):26–28, 1996.

[153] E. Rignot and R. Kwok. Extraction of textural features in SAR images: Statistical model and sensitivity. In 10th Annual International Geoscience and Remote Sensing Symposium, 1990. (IGARSS ’90) ‘Remote Sensing Science for the Nineties’, pages 1979–1982, Washing DC, USA, 1990. IEEE Press.

[154] Daniel Rivero, Juan R. Rabual, Julin Dorado, and Alejandro Pazos. Using genetic programming for character discrimination in damaged documents. In Gunther R. Raidl et al, editor, Applications of Evolutionary Computing, volume 3005 of Lecture Notes in Computer Science, pages 349–358. Springer Berlin / Heidelberg, 2004. [ 155] RMIT-GP. Version 1.3.1. http://www.cs.rmit.edu.au/∼vc/rmitgp/, 2002. School of Computer Science and Information Technology, RMIT University. Visited:01/01/2003.

[156] Simon C. Roberts and Daniel Howard. Genetic programming for image analysis: Orien- tation detection. In Darrell Whitley, David Goldberg, Erick Cantu-Paz, Lee Spector, Ian Parmee, and Hans-Georg Beyer, editors, Proceedings of the Genetic and Evolu- tionary Computation Conference (GECCO-2000), pages 651–657, Las Vegas, Nevada, USA, 10-12 July 2000. Morgan Kaufmann.

[157] Brian J. Ross, Frank Fueten, and Dmytro Yashkir. Automatic mineral identification using genetic programming. Machine Vision and Applications, 13(2):61–69, 2001.

[158] Brian J. Ross, Frank Fueten, and Dmytro Y. Yashkir. Edge detection of petrographic images using genetic programming. In Darrell Whitley, David Goldberg, Erick Cantu- Paz, Lee Spector, Ian Parmee, and Hans-Georg Beyer, editors, Proceedings of the Ge- netic and Evolutionary Computation Conference (GECCO-2000), pages 658–665, Las Vegas, Nevada, USA, 10-12 July 2000. Morgan Kaufmann.

[159] Brian J. Ross, Anthony G. Gualtieri, Frank Fueten, and Paul Budkewitsch. Hyperspec- tral image analysis using genetic programming. Applied Soft Computing, 5(2):147–156, January 2005.

[160] Conor Ryan, J.J. Collins, and Michael O’Neill. Grammatical evolution: Evolving programs for an arbitrary language. In Lecture Notes in Computer Science 1391, Proceedings of the First European Workshop on Genetic Programming, pages 83–95. Springer-Verlag, 1998.

[161] Paul Scheunders. A comparison of clustering algorithms applied to color image quan- tization. Pattern Recognition Letters, 18(11-13):1379–1384, 1997.

[162] Marc Schoenauer, Michele Sebag, Francois Jouve, Bertrand Lamy, and Habibou Mai- tournam. Evolutionary identification of macro-mechanical models. In Peter J. Angeline and K. E. Kinnear, Jr., editors, Advances in Genetic Programming 2, chapter 23, pages 467–488. MIT Press, Cambridge, MA, USA, 1996.

[163] Hans-Paul Schwefel and G ̈unter Rudolph. Contemporary evolution strategies. In Fed- erico Moran et al., editor, Proceedings of the Third European Conference on Advances in Artificial Life, pages 893–907, London, UK, 1995. Springer-Verlag.

[164] Shinichi Shirakawa, Shiro Nakayama, and Tomoharu Nagao. Genetic image network for image classification. In Mario Giacobini, editor, EvoWorkshops ’09: Proceedings of the EvoWorkshops 2009 on Applications of Evolutionary Computing, pages 395–404, Berlin, Heidelberg, 2009. Springer-Verlag.

[165] Reena Singh. Wavelets transform, Gabor transform and spatial gray level dependence method: A comparative study for texture analysis. Master’s thesis, Department of Electrical Engineering, Mayaguez Campus, University of Puerto Rico., 1998.

[166] Tarundeep Singh, Nawwaf Kharma, Mohmmad Daoud, and Rabab Ward. Genetic programming based image segmentation with applications to biomedical object detec- tion. In Gunther Raidl et al., editor, GECCO ’09: Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, pages 1123–1130, New York, NY, USA, 2009. ACM.

[167] Irwin Edward Sobel. Camera models and machine perception. PhD thesis, Department of Computer Science, Stanford, CA, USA, 1970.

[168] Andy Song. Fast video analysis by genetic programming. In Jun Wang, editor, 2008 IEEE World Congress on Computational Intelligence, pages 3237–3243, Hong Kong, 1-6 June 2008. IEEE Computational Intelligence Society, IEEE Press.

[169] Andy Song and Vic Ciesielski. Texture analysis by genetic programming. In Proceedings of the 2004 IEEE Congress on Evolutionary Computation, pages 2092–2099, Portland, Oregon, 20-23 June 2004. IEEE Press.

[170] Andy Song and Vic Ciesielski. Texture segmentation by genetic programming. Evolu- tionary Computation, 16(4):461–481, 2008.

[171] Andy Song, Vic Ciesielski, and Hugh Williams. Texture classifiers generated by genetic programming. In David B. Fogel, Mohamed A. El-Sharkawi, Xin Yao, Garry Green- wood, Hitoshi Iba, Paul Marrow, and Mark Shackleton, editors, Proceedings of the 2002 Congress on Evolutionary Computation (CEC2002), pages 243–248. IEEE Press, 2002.

[172] Andy Song and Danny Fang. A robust method of detecting moving objects in videos evolved by genetic programming. In Martin Keijzer et al., editor, GECCO ’08: Pro- ceedings of the 10th Annual Conference on Genetic and Evolutionary Computation, pages 1649–1656, New York, NY, USA, 2008. ACM.

[173] Andy Song, Thomas Loveard, and Vic Ciesielski. Towards genetic programming for texture classification. In M. Stumptner, D. Corbett, and M. Brooks, editors, Pro- ceedings of the 14th International Joint Conference on Artificial Intelligence AI 2001: Advances in Artificial Intelligence, volume 2256 of Lecture Notes in Computer Science, pages 461–472, Adelaide, Australia, December 10-14 2001. Springer-Verlag.

[174] Terence Soule and James A. Foster. Effects of code growth and parsimony pressure on populations in genetic programming. Evol. Comput., 6:293–309, December 1998.

[175] Lee Spector and Alan Robinson. Genetic programming and autoconstructive evolution with the push programming language. Genetic Programming and Evolvable Machines, 3(1):7–40, March 2002.

[176] Stephen A. Stanhope and Jason M. Daida. Genetic programming for automatic target classification and recognition in synthetic aperture radar imagery. In V. William Porto, N. Saravanan, D. Waagen, and A. E. Eiben, editors, Evolutionary Programming VII: Proceedings of the Seventh Annual Conference on Evolutionary Programming, volume 1447 of LNCS, pages 735–744, Mission Valley Marriott, San Diego, California, USA, 25-27 March 1998. Springer-Verlag.

[177] Chengjun Sun and William G. Wee. Neighboring gray level dependence matrix for texture classification. Computer Vision, Graphics, and Image Processing, 23(3):341– 352, 1983.

[178] Walter Alden Tackett. Genetic programming for feature discovery and image discrim- ination. In Stephanie Forrest, editor, Proceedings of the 5th International Conference on Genetic Algorithms, (ICGA-93), pages 303–309, University of Illinois at Urbana- Champaign, 17-21 July 1993. Morgan Kaufmann.

[179] Xuejun Tan, B. Bhanu, and Yingqiang Lin. Fingerprint classification based on learned features. IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, 35(3):287 –300, August 2005.

[180] Astro Teller and Manuela Veloso. A controlled experiment: Evolution for learning dif- ficult image classification. In Seventh Portuguese Conference On Artificial Intelligence, volume 990 of Lecture Notes in Computer Science, pages 165–176, Funchal, Madeira Island, Portugal, October 3-6 1995. Springer-Verlag.

[181] Bea Thai and Glenn Healey. Modeling and classifying symmetries using a multiscale opponent color representation. IEEE Transactions on Pattern Analysis and Machine Intelligence, 20(11):1224–1235, November 1998.

[182] Cuong To and T.D. Pham. Analysis of cardiac imaging data using decision tree based parallel genetic programming. In Proceedings of 6th International Symposium on Image and Signal Processing and Analysis (ISPA 2009), pages 317–320, September 2009. IEEE Press.

[183] Marco Tomassini, Leonardo Vanneschi, Philippe Collard, and Manuel Clergue. A study of fitness distance correlation as a difficulty measure in genetic programming. Evolu- tionary Computation, 13(2):213–239, June 2005.

[184] Leonardo Trujillo and Gustavo Olague. Automated design of image operators that detect interest points. Evolutionary Computation, 16(4):483–507, 2008.

[185] M. Tuceryan and A.K. Jain. Texture segmentation using Voronoi polygons. IEEE Transactions on Pattern Analysis and Machine Intelligence, 12(2):211–216, 1990.

[186] M. Tuceryan and A.K. Jain. Handbook of Pattern Recognition and Computer Vision, chapter 2 Texture Analysis, pages 235–276. World Scientific, Singapore, 1993.

[187] C. Umarani, L. Ganesan, and S. Radhakrishnan. Analysis of skin images using texture descriptor by a combined statistical and structural approach. International Journal of Imaging System and Technology, 17(6):359–366, April 2008.

[188] Michael Unser. Sum and difference histograms for texture classification. IEEE Trans- actions on Pattern Analysis and Machine Intelligence, 8(1):118–125, January 1986.

[189] H. Utku, H. Koksiel, and R. Ozkara. Classification of barleys based on malting quality by image analysis. Journal of Institute of Brewing, 104(1):351–354, 1998.

[190] Leonardo Vanneschi, Denis Rochat, and Marco Tomassini. Multi-optimization improves genetic programming generalization ability. In Dirk Thierens et al., editor, GECCO ’07: Proceedings of the 9th Annual Conference on Genetic and Evolutionary Computation, volume 2, pages 1759–1759, London, 7-11 July 2007. ACM Press.BIBLIOGRAPHY 169

[191] Leonardo Vanneschi, Marco Tomassini, Philippe Collard, and Sbastien Vrel. Negative slope coefficient: A measure to characterize genetic programming fitness landscapes. In Pierre Collet et al., editor, Genetic Programming, Lecture Notes in Computer Science, pages 178–189. Springer Berlin, Heidelberg, 2006.

[192] Manik Varma and Andrew Zisserman. A statistical approach to texture classification from single images. International Journal of Computer Vision, 62(1):61–81, 2005.

[193] E.J. Vladislavleva, G.F. Smits, and D. den Hertog. Order of nonlinearity as a com- plexity measure for models generated by symbolic regression via pareto genetic pro- gramming. IEEE Transactions on Evolutionary Computation, 13(2):333 –349, April 2009.

[194] T Wagner. Texture analysis. In Bernd Jahne, Hoirst Haussecker, and Peter Geissler, editors, Handbook of Computer Vision and Applications, volume 2, chapter 12, pages 276–308. Morgan Kaufmann Publishers Inc., San Francisco, CA, USA, 1999.

[195] Jun Wang and Ying Tan. A novel genetic programming based morphological image analysis algorithm. In Jurgen Branke et al., editor, GECCO ’10: Proceedings of the 12th Annual Conference on Genetic and Evolutionary Computation, pages 979–980, New York, NY, USA, 2010. ACM.

[196] Ukrit Watchareeruetai, Yoshinori Takeuchi, Tetsuya Matsumoto, Hiroaki Kudo, and Noboru Ohnishi. Evaluations of feature extraction programs synthesized by redundancy-removed linear genetic programming: A case study on the lawn weed de- tection problem. Journal of Information Processing, 18(1):164–174, 2010.

[197] A. Weber. The USC-SIPI Image Database vers 3. Technical report, University of Southern Carlifornia, Department of Electrical Engineering, 1992.

[198] J. Weszka, C. Dyer, and A. Rosenfield. A comparative study of texture measures for terrian classification. IEEE Transactions on Systems, Man, and Cybernetics, 6(1):269– 285, 1976.

[199] Jay F. Winkeler and B. S. Manjunath. Genetic programming for object detection. In John R. Koza, Kalyanmoy Deb, Marco Dorigo, David B. Fogel, Max Garzon, Hitoshi Iba, and Rick L. Riolo, editors, Genetic Programming 1997: Proceedings of the Second Annual Conference, pages 330–335, Stanford University, CA, USA, 13-16 July 1997. Morgan Kaufmann.

[200] Ian Witten and Eibe Frank. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Academic Press, 2000.

[201] Jiangang Yu and Bir Bhanu. Evolutionary feature synthesis for facial expression recog- nition. Pattern Recognition Letters, 27(11):1289–1298, 2006.

[202] Byoung-Tak Zhang and Heinz Mühlenbein. Balancing accuracy and parsimony in ge- netic programming. Evolutionary Computing, 3:17–38, March 1995.

[203] J. Zhang, M. Marszaek, S. Lazebnik, and C. Schmid. Local features and kernels for classification of texture and object categories: A comprehensive study. International Journal of Computer Vision, 73(2):213–238, 2007.

[204] Mengjie Zhang and Victor Ciesielski. Genetic programming for multiple class object detection. In Norman Foo, editor, 12th Australian Joint Conference on Artificial Intel- ligence, volume 1747 of Lecture Notes in Artificial Intelligence, pages 180–192, Sydney, Australia, 6-10 December 1999. Springer-Verlag.

[205] Yang Zhang and Peter I. Rockett. Evolving optimal feature extraction using multi- objective genetic programming: A methodology and preliminary study on edge de- tection. In Hans-Georg Beyer et al., editor, GECCO ’05: Proceedings of the 2005 Conference on Genetic and Evolutionary Computation, pages 795–802, New York, NY, USA, 2005. ACM.

[206] Yang Zhang and Peter I. Rockett. A generic multi-dimensional feature extraction method using multiobjective genetic programming. Evolutionary Computing, 17(1):89– 115, 2009.

[207] Jian Zou and Chuan-Cai Liu. Texture classification by matching co-occurrence matrices on statistical manifolds. In Computer and Information Technology (CIT), 2010 IEEE 10th International Conference on, pages 1–7, July 2010.

Links

Full Text

http://researchbank.rmit.edu.au/eserv/rmit:160245/Lam.pdf

intern file

Sonstige Links