ABSTRACT
Texture classification aims at categorising instances that have a similar repetitive pattern. In computer vision, texture classification represents a fundamental element in a wide variety of applications, which can be performed by detecting texture primitives of the different classes. Using image descriptors to detect prominent features has been widely adopted in computer vision. Building an effective descriptor becomes more challenging when there are only a few labelled instances. This paper proposes a new Genetic Programming (GP) representation for evolving an image descriptor that operates directly on the raw pixel values and uses only two instances per class. The new method synthesises a set of mathematical formulas that are used to generate the feature vector, and the classification is then performed using a simple instance-based classifier. Determining the length of the feature vector is automatically handled by the new method. Two GP and nine well-known non-GP methods are compared on two texture image data sets for texture classification in order to test the effectiveness of the proposed method. The proposed method is also compared to three hand-crafted descriptors namely domain-independent features, local binary patterns, and Haralick texture features. The results show that the proposed method has superior performance over the competitive methods.
- W. Albukhanajer, J. Briffa, and Y. Jin. Evolutionary multiobjective image feature extraction in the presence of noise. IEEE Transactions on Cybernetics, pages 1--12, 2014.Google Scholar
- D. Androutsos, K. Plataniotis, and A. Venetsanopoulos. Distance measures for color image retrieval. In Proceedings of the International Conference on Image Processing, volume 2, pages 770--774. IEEE, 1998.Google ScholarCross Ref
- P. Brodatz. Textures: A Photographic Album for Artists and Designers. Dover Publications, 1999.Google Scholar
- J. Dem\vsar. Statistical comparisons of classifiers over multiple data sets. Journal of Machine Learning Research, 7:1--30, 2006. Google ScholarDigital Library
- M. Hall, E. Frank, G. Holmes, B. Pfahringer, P. Reutemann, and I. H. Witten. The WEKA data mining software: An update. SIGKDD Explorations Newsletter, 11(1):10--18, 2009. Google ScholarDigital Library
- R. Haralick, K. Shanmugam, and I. Dinstein. Textural features for image classification. IEEE Transactions on Systems, Man and Cybernetics, SMC-3(6):610--621, 1973.Google ScholarCross Ref
- S. Hindmarsh, P. Andreae, and M. Zhang. Genetic programming for improving image descriptors generated using the scale-invariant feature transform. In Proceedings of the 27th International Conference on Image and Vision Computing New Zealand, pages 85--90. ACM, 2012. Google ScholarDigital Library
- G. Holmes, B. Pfahringer, R. Kirkby, E. Frank, and M. Hall. Multiclass alternating decision trees. In Proceedings of the 13th European Conference on Machine Learning, pages 161--172. Springer, 2002. Google ScholarDigital Library
- S. Keerthi and C.-J. Lin. Asymptotic behaviors of support vector machines with gaussian kernel. Neural Computation, 15(7):1667--1689, 2003. Google ScholarDigital Library
- J. R. Koza. Genetic Programming: On the Programming of Computers by Means of Natural Selection. MIT Press, 1992. Google ScholarDigital Library
- G. Kylberg. 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.Google Scholar
- K.-L. Lim and H. Galoogahi. Shape classification using local and global features. In Proceedings of the 4th Pacific-Rim Symposium on Image and Video Technology, pages 115--120. IEEE Computer Society, 2010. Google ScholarDigital Library
- T. Loveard and V. Ciesielski. Representing classification problems in genetic programming. In Proceedings of the IEEE Congress on Evolutionary Computation, volume 2, pages 1070--1077. IEEE, 2001.Google ScholarCross Ref
- D. G. Lowe. Object recognition from local scale-invariant features. In Proceedings of the International Conference on Computer Vision, pages 1150--1157. IEEE, 1999. Google ScholarDigital Library
- S. Luke. Essentials of Metaheuristics. Lulu, second edition, 2013.Google Scholar
- D. J. Montana. Strongly typed genetic programming. Evolutionary Computation, 3(2):199--230, 1995. Google ScholarDigital Library
- T. Ojala, M. Pietik\"ainen, and D. Harwood. Performance evaluation of texture measures with classification based on Kullback discrimination of distributions. In Proceedings of the 12th International Conference on Pattern Recognition, volume 1, pages 582--585. IEEE, 1994.Google ScholarCross Ref
- G. Olague and L. Trujillo. Evolutionary-computer- assisted design of image operators that detect interest points using genetic programming. Image and Vision Computing, 29(7):484--498, 2011. Google ScholarDigital Library
- C. B. Perez and G. Olague. Evolutionary learning of local descriptor operators for object recognition. In Proceedings of the 11th Annual Conference on Genetic and Evolutionary Computation, pages 1051--1058. ACM, 2009. Google ScholarDigital Library
- W. Smart and M. Zhang. Using genetic programming for multiclass classification by simultaneously solving component binary classification problems. In Proceedings of the 8th European Conference on Genetic Programming, pages 227--239. Springer, 2005. Google ScholarDigital Library
- A. Song, T. Loveard, and V. Ciesielski. Towards genetic programming for texture classification. In Proceedings of the 14th Australian Joint Conference on Artificial Intelligence, volume 2256 of Lecture Notes in Computer Science, pages 461--472. Springer, 2001. Google ScholarDigital Library
- W. A. Tackett. Genetic programming for feature discovery and image discrimination. In Proceedings of the 5th International Conference on Genetic Algorithms, pages 303--311, 1993. Google ScholarDigital Library
- S. Trenn. Multilayer perceptrons: Approximation order and necessary number of hidden units. IEEE Transactions on Neural Networks, 19(5):836--844, 2008. Google ScholarDigital Library
- L. Trujillo and G. Olague. Automated design of image operators that detect interest points. Evolutionary Computation, 16(4):483--507, 2008. Google ScholarDigital Library
- M. Tuceryan and A. K. Jain. Texture analysis. In C. H. Chen, L. F. Pau, and P. S. P. Wang, editors, Handbook of Pattern Recognition & Computer Vision, pages 235--276. World Scientific, 1993. Google ScholarDigital Library
- P. Whigham and G. Dick. Implicitly controlling bloat in genetic programming. IEEE Transactions on Evolutionary Computation, 14(2):173--190, 2010. Google ScholarDigital Library
- F. Wilcoxon. Individual comparisons by ranking methods. Biometrics Bulletin, 1(6):80--83, 1945.Google ScholarCross Ref
- I. H. Witten and E. Frank. Data Mining: Practical Machine Learning Tools and Techniques. Morgan Kaufmann, second edition, 2005. Google ScholarDigital Library
- L. Zhang, L. Jack, and A. Nandi. Extending genetic programming for multi-class classification by combining k-nearest neighbor. In Proceedings of the IEEE International Conference on Acoustics, Speech, and Signal Processing, volume 5, pages v/349--v/352, 2005.Google Scholar
- M. Zhang and V. Ciesielski. Genetic programming for multiple class object detection. In Proceedings of the 12th Australian Joint Conference on Artificial Intelligence, volume 1747 of Lecture Notes in Computer Science, pages 180--192. Springer, 1999. Google ScholarDigital Library
- M. Zhang, V. Ciesielski, and P. Andreae. A domain-independent window approach to multiclass object detection using genetic programming. EURASIP Journal on Advances in Signal Processing, 2003(8):841--859, 2003. Google ScholarDigital Library
- M. Zhang and M. Johnston. A variant program structure in tree-based genetic programming for multiclass object classification. In Evolutionary Image Analysis and Signal Processing, volume 213 of Studies in Computational Intelligence, pages 55--72. Springer, 2009.Google ScholarCross Ref
Index Terms
- Evolutionary Image Descriptor: A Dynamic Genetic Programming Representation for Feature Extraction
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