Abstract
Image feature selection is formulated as an optimization problem. When traditional genetic algorithm is used for selecting image feature, it may bring problems of local convergence or precocious puberty because of using a fixed probability of crossover operator and mutation operator. First, the paper gave a brief introduction to image feature selection regarding the purposes, tasks, and commonly used algorithms. Then, the paper improved genetic operator of genetic algorithm, and parallel computing was used to genetic algorithm, to enhance the performance of image feature selection. Finally, experiments show that the improved genetic algorithm is convergent and effective, and applied to image feature selection is successful.
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Acknowledgments
The work described in this paper was supported by the Natural Science Foundation Project of CQ CSTC under Grant No. 2010BB2284; the First Batch of Supporting Program for University Excellent Talents in Chongqing; and Research Project of Chongqing University of Science and Technology under Grant No. CK2011Z17. The authors are grateful for the anonymous reviewers who made constructive comments.
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© 2013 Springer-Verlag London
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Lei, L., Peng, J., Yang, B. (2013). Image Feature Selection Based on Genetic Algorithm. In: Zhong, Z. (eds) Proceedings of the International Conference on Information Engineering and Applications (IEA) 2012. Lecture Notes in Electrical Engineering, vol 219. Springer, London. https://doi.org/10.1007/978-1-4471-4853-1_101
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DOI: https://doi.org/10.1007/978-1-4471-4853-1_101
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