Fast texture segmentation using genetic programming

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Andy Song and Vic Ciesielski: Fast texture segmentation using genetic programming. Proceedings of the 2003 Congress on Evolutionary Computation CEC2003, pp. 2126-2133, IEEE Press, 8-12 December 2003.



This paper presents a method which extends the use of genetic programming (GP) to a complex domain, texture segmentation. By this method, segmentation tasks are performed by texture classifiers which are evolved by the GP. Small cutouts sampled from images of various textures are used for the evolution. The generated classifiers directly use pixel values as input. Based on these classifiers an algorithm which uses a voting strategy to partition texture regions is developed. The results of the investigation indicate that the proposed method is able to accurately identify the boundaries between different texture regions, even if the boundaries are not regular. The method can segment two textures as well as multiple textures. Furthermore, fast segmentation can be achieved. The speed of the proposed texture segmentation method can be a hundred times faster than conventional methods.

Extended Abstract


Used References

Phil Brodatz. Textures: A Photographic Album for Artists and Designers. Dover, NY, 1966.

K.I. Chang, K.W. Bowyer and M. Sivagurunath. Evaluation of texture segmentation algorithms. In IEEE Computer Society Conference on Computer Vision and Pattern Recognition, 1999, Volume 1. pages 294-299, 1999.

Kan-Min Chen and Shu-Yuan Chen. Color texture segmentation using feature distributions. Pattern Recognition Letters, Volume 23, Number 7, pages 755-771, May 2002.

I., De Falco, E., Tarantino and A., Delia Cioppa. Unsupervised spectral pattern recognition for multispectral images by means of a genetic programming approach. In Proceedings of the 2002 Congress on Evolutionary Computation, 2002, pages 231-236, May 2002.

R. M. Haralick, K. Shanmugam and I. Dinstein. Textural features for image classification. IEEE Transactions On Systems, Man, and Cybernetics, Volume SMC-3, Number 6, pages 610-621, November 1973.

Anil K. Jain and Kalle Karu. Learning texture discrimination masks. IEEE Transactions on Pattern Analysis and Machine Intelligence, Volume 18, pages 195-205, 1996.

John R. Koza. Genetic Programming III: Aarwinian Invention and Problem Solving. Morgan Kaufmann, San Francisco, 1999.

Thomas Loveard and Vic Ciesielski. Representing classification problems in genetic programming. In Proceedings of the 2001 Congress on Evolutionary Computation CEC2001, pages 27-30. IEEE Neural Network Council (NNC), Evolutionary Programming Society (EPS), Institution of Electrical Engineers (IEE), IEEE Press, May 27-302001.

R.W. Picard, T. Kabir and F Liu. Real-time recognition with the entire Brodatz texture database. In Computer Vision and Pattern Recognition, 1993. Proceedings CVPR '93., 1993 IEEE Computer Society Conference on, pages 638-639, 1993.

RiccardoPoli. Genetic programming for feature detection and image segmentation. In T. C. Fogarty (editor), Evolutionary Computing. Springer-Vcrlag, 1996.

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

A. Song, V. Ciesielski and H.E Williams. Texture classifiers generated by genetic programming. In Evolutionary Computation, 2002. CEC '02. Proceedings of the 2002 Congress on. Volume 1, pages 243 -248, May 2002.

Andy Song, Thomas Loveard and Vic Ciesielski. Towards genetic programming for texture classification. Lecture Notes in Computer Science, Volume 2256, pages 461-472, 2001.

Walter Alden Tackett. Genetic generation of "dendritic" trees for image classification. In Proceedings of WCNN93, IV pages 646-649. IEEE Press, July 1993.

Mihran Tuceryan and Anil K. Jain. Texture analysis. In C. H. Chen, L. F. Pau and P. S. P. Wang (editors). Handbook of Pattern Recognition and Computer Vision, Chapter 2, pages 235-276. World Scientific, Singapore, 1993.

Ian H. Witten and Eibe Frank. Data Mining: Practical Machine Learning Tools and Techniques with Javaim- plementations. San Francisco, Calif. : Morgan Kauf-mann, 2000.

Mengjie Zhang and Victor Ciesielski. Genetic programming for multiple class object detection. In Norman Foo (editor), Proceedings of the 12th Australian Joint Conference on Artificial Intelligence (AI'99), pages 180-192, Sydney, Australia, December 1999. Springer-Verlag Berlin Heidelberg. Lecture Notes in Artificial Intelligence (LNAI Volume 1747).


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