Evolution of Psuedo-colouring Algorithms for Image Enhancement with Interactive Genetic Programming
Inhaltsverzeichnis
Reference
R. Poli and S. Cagnoni: Evolution of Psuedo-colouring Algorithms for Image Enhancement with Interactive Genetic Programming. Proceedings of the Second International Conference on Genetic Programming, GP'97, pp. 269-277, Stanford, July 1997. Morgan Kaufmann.
DOI
Abstract
In this paper we present an approach to the interactive development of programs for image enhancement with Genetic Programming (GP) based on pseudo-colour transformations. In our approach the user drives GP by deciding which individual should be the winner in tournament selection. The presence of the user does not only allow running GP without a fitness function but it also transforms GP into a very efficient search procedure capable of producing effective solutions to real-life problems in only hundreds of evaluations. In the paper we also propose a strategy to further reduce user interaction: we record the choices made by the user in interactive runs and we later use them to build a model which can replace him/her in longer runs. Experimental results with interactive GP and with our user-modelling strategy are also reported.
Extended Abstract
Bibtex
Used References
[Andre, 1994] Andre, D. (1994). Automatically defined fea- tures: The simultaneous evolution of 2-dimensional feature detectors and an algorithm for using them. In Kinn- ear, Jr., K. E., editor, Advances in Genetic Programming, chapter 23, pages 477–494. MIT Press.
[Biles, 1994] Biles, J. A. (1994). GenJam: A genetic al- gorithm for generating jazz solos. In Proceedings of the 1994 International Computer Music Conference, ICMA, San Francisco.
[Cagnoni et al., 1991] Cagnoni, S., Caramella, D., Marin, E., and Valli, G. (1991). Computer assisted integration and display of diagnostic features in MR spin echo multi echo sequences. In Lemke, H., Rhodes, M., Jaffe, C., and Felix, R., editors, Computer-Aided Radiology (Proc. CAR ’91), pages 40–45, Berlin. Springer-Verlag.
[Daida et al., 1996] Daida, J. M., Hommes, J. D., Bersano- Begey, T. F., Ross, S. J., and Vesecky, J. F. (1996). Algo- rithm discovery using the genetic programming paradigm: Extracting low-contrast curvilinear features from SAR im- ages of arctic ice. In Angeline, P. J. and Kinnear, Jr., K. E., editors, Advances in Genetic Programming 2, chapter 21, pages 417–442. MIT Press, Cambridge, MA, USA.
[Daida et al., 1995] Daida, J. M., Hommes, J. D., Ross, S. J., and Vesecky, J. F. (1995). Extracting curvilinear features from SAR images of arctic ice: Algorithm discovery us- ing the genetic programming paradigm. In Proceedings of IEEE International Geoscience and Remote Sensing, Flo- rence, It, volume 1, pages 673–675.
[Das et al., 1994] Das, S., Franguidakis, T., Papka, M., De- Fanti, T. A., and Sandin, D. J. (1994). A genetic pro- gramming application in virtual reality. In Proceedings of the first IEEE Conference on Evolutionary Computation, volume 1, pages 480–484, Orlando, Florida, USA. IEEE Press. Part of 1994 IEEE World Congress on Computa- tional Intelligence, Orlando, Florida.
[Dawkins, 1987] Dawkins, R. (1987). The evolution of evolvability. In Proceedings of the Interdisciplinary Work- shop on the Synthesis and Simulation of Living Systems (ALIFE ’87), pages 201–220, Los Alamos, NM, USA.
[Fonseca and Fleming, 1995] Fonseca, C. and Fleming, P. (1995). An overview of evolutionary algorithms in multiobjective optimization. Evolutionary Computation, 3(1):1–17.
[Goldberg, 1989] Goldberg, D. E. (1989). Genetic Algo- rithms in Search, Optimization, and Machine Learning. Addison-Wesley, Reading, Massachusetts.
[Graf and Banzhaf, 1995] Graf, J. and Banzhaf, W. (1995). Expansion operator for interactive evolution. In Proceed- ings of the IEEE Conference on Evolutionary Computa- tion, volume 2, pages 798–802. IEEE.
[Greenwood et al., 1996] Greenwood, G. W., Hu, X., and D’Ambrosio, J. G. (1996). Fitness functions for mul- tipleobjective optimisation problems: Combining prefer- ences with Pareto rankings. In Belew, R. K. and Vose, M., editors, Foundations of Genetic Algorithms IV, San Diego.
[Gruau, 1994] Gruau, F. (1994). Neural Network Synthe- sis using Cellular Encoding and the Genetic Algorithm. PhD thesis, Laboratoire de l’Informatique du Parallilisme, Ecole Normale Supirieure de Lyon, France.
[Gruau and Quatramaran, 1996] Gruau, F. and Quatramaran, K. (1996). Cellular encoding for interactive evolutionary robotics. Cognitive Science Research Paper 425, School of Cognitive and Computing Sciences, University of Sussex, Falmer, Brighton, Sussex, UK.
[Harris and Buxton, 1996] Harris, C. and Buxton, B. (1996). Evolving edge detectors with genetic programming. In Koza, J. R., Goldberg, D. E., Fogel, D. B., and Riolo, R. L., editors, Genetic Programming 1996: Proceedings of the First Annual Conference, pages 309–315, Stanford University, CA, USA. MIT Press.
[Johnson et al., 1994] Johnson, M. P., Maes, P., and Darrell, T. (1994). Evolving visual routines. In Brooks, R. A. and Maes, P., editors, ARTIFICIAL LIFE IV, Proceedings of the fourth International Workshop on the Synthesis and Simu- lation of Living Systems, pages 198–209, MIT, Cambridge, MA, USA. MIT Press.
[Koza, 1992] Koza, J. R. (1992). A genetic approach to the truck backer upper problem and the inter-twined spi- ral problem. In Proceedings of IJCNN International Joint Conference on Neural Networks, volume IV, pages 310– 318. IEEE Press.
[Koza, 1994] Koza, J. R. (1994). Genetic Programming II: Automatic Discovery of Reusable Programs. MIT Pres, Cambridge, Massachusetts.
[Langdon, 1995] Langdon, W. B. (1995). Pareto, population partitioning, price and genetic programming. Research Note RN/95/29, University College London, Gower Street, London WC1E 6BT, UK.
[Poli, 1996a] Poli, R. (1996a). Genetic programming for fea- ture detection and image segmentation. In Fogarty, T. C., editor, Evolutionary Computing, number 1143 in Lecture Notes in Computer Science, pages 110–125. Springer- Verlag, University of Sussex, UK.
[Poli, 1996b] Poli, R. (1996b). Genetic programming for image analysis. In Koza, J. R., Goldberg, D. E., Fogel, D. B., and Riolo, R. L., editors, Genetic Programming 1996: Proceedings of the First Annual Conference, pages 363–368, Stanford University, CA, USA. MIT Press.
[Sims, 1991] Sims, K. (1991). Artificial evolution for com- puter graphics. ACM Computer Graphics, 25(4):319–328. SIGGRAPH ’91 Proceedings.
[Spector and Alpern, 1995] Spector, L. and Alpern, A. (1995). Induction and recapitulation of deep musical struc- ture. In Proceedings of International Joint Conference on Artificial Intelligence, IJCAI’95 Workshop on Music and AI, Montreal, Quebec, Canada.
[Surry et al., 1995] Surry, P., Radcliffe, N., and Boyd, I. (1995). A multi-objective approach to constrained opti- misation of gas supply networks: the COMOGA method. In Fogarty, T. C., editor, Evolutionary Computing, number 993 in Lecture Notes in Computer Science, pages 166– 180, Sheffield, UK. Springer-Verlag.
[Tackett, 1993] Tackett, W. A. (1993). Genetic generation of “dendritic” trees for image classification. In Proceedings of WCNN93, pages IV 646–649. IEEE Press.
[Teller and Veloso, 1995] Teller, A. and Veloso, M. (1995). PADO: Learning tree structured algorithms for orchestra- tion into an object recognition system. Technical Re- port CMU-CS-95-101, Department of Computer Science, Carnegie Mellon University, Pittsburgh, PA, USA.
[Webb, 1988] Webb, S. (1988). The physics of medical imag- ing. Institute of Physics Publishing. (Reprinted with cor- rections 1993).
Links
Full Text
http://cswww.essex.ac.uk/staff/poli/papers/Poli-GP1997-UM.pdf
