A Domain Independent Genetic Programming Approach to Automatic Feature Extraction for Image Classification: Unterschied zwischen den Versionen
| Zeile 14: | Zeile 14: | ||
== Used References == | == Used References == | ||
P. Espejo, S. Ventura, and F. Herrera, A survey on the application of genetic programming to classification, Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, vol. 40, no. 2, pp. 121-144, 2010. http://dx.doi.org/10.1109/TSMCC.2009.2033566 | P. Espejo, S. Ventura, and F. Herrera, A survey on the application of genetic programming to classification, Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, vol. 40, no. 2, pp. 121-144, 2010. http://dx.doi.org/10.1109/TSMCC.2009.2033566 | ||
| + | |||
| + | M. Zhang and U. Bhowan, Program size and pixel statistics in genetic programming for object detection, in Applications of Evolutionary Computing, ser. Lecture Notes in Computer Science, G. R. Raidl, S. Cagnoni, J. Branke, D. W. Corne, R. Drechsler, Y. Jin, C. G. Johnson, P. Machado, E. Marchiori, F. Rothlauf, G. D. Smith, and G. Squillero, Eds. Springer Berlin/Heidelberg, 2004, vol. 3005, pp. 379-388, http://link.springer.com/chapter/10.1007%2F978-3-540-24653-4_39 | ||
Version vom 23. November 2014, 18:35 Uhr
Inhaltsverzeichnis
Reference
Daniel Atkins and Kourosh Neshatian and Mengjie Zhang: A Domain Independent Genetic Programming Approach to Automatic Feature Extraction for Image Classification. Proceedings of the 2011 IEEE Congress on Evolutionary Computation, pp. 238-245, IEEE Press, 5-8 June 2011.
DOI
http://dx.doi.org/10.1109/CEC.2011.5949624
Abstract
In this paper we explore the application of Genetic Programming (GP) to the problem of domain-independent image feature extraction and classification. We propose a new GP based image classification system that extracts image features autonomously, and compare its performance against a baseline GP-based classifier system that uses human-extracted features. We found that the proposed system has a similar performance to the baseline system, and that GP is capable of evolving a single program that can both extract useful features and use those features to classify an image.
Extended Abstract
Bibtex
Used References
P. Espejo, S. Ventura, and F. Herrera, A survey on the application of genetic programming to classification, Systems, Man, and Cybernetics, Part C: Applications and Reviews, IEEE Transactions on, vol. 40, no. 2, pp. 121-144, 2010. http://dx.doi.org/10.1109/TSMCC.2009.2033566
M. Zhang and U. Bhowan, Program size and pixel statistics in genetic programming for object detection, in Applications of Evolutionary Computing, ser. Lecture Notes in Computer Science, G. R. Raidl, S. Cagnoni, J. Branke, D. W. Corne, R. Drechsler, Y. Jin, C. G. Johnson, P. Machado, E. Marchiori, F. Rothlauf, G. D. Smith, and G. Squillero, Eds. Springer Berlin/Heidelberg, 2004, vol. 3005, pp. 379-388, http://link.springer.com/chapter/10.1007%2F978-3-540-24653-4_39
