Image Segmentation: A Survey of Methods Based on Evolutionary Computation

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@InProceedings{Liang:2014:SEAL,

• author = "Yuyu Liang and Mengjie Zhang and Will N. Browne",
• title = "Image Segmentation: A Survey of Methods Based on Evolutionary Computation",
• booktitle = "Proceedings 10th International Conference on Simulated Evolution and Learning, SEAL 2014",
• year = "2014",
• editor = "Grant Dick and Will N. Browne and Peter Whigham and Mengjie Zhang and Lam Thu Bui and Hisao Ishibuchi and Yaochu Jin and Xiaodong Li and Yuhui Shi and Pramod Singh and Kay Chen Tan and Ke Tang",
• volume = "8886",
• series = "Lecture Notes in Computer Science",
• pages = "847--859",
• address = "Dunedin, New Zealand",
• month = dec # " 15-18",
• publisher = "Springer",
• keywords = "genetic algorithms, genetic programming, Evolutionary Computer Vision, Image Segmentation, Evolutionary Computation",
• isbn13 = "978-3-319-13562-5",
• DOI = "doi:10.1007/978-3-319-13563-2_71",
• abstract = "Image segmentation is mainly used as a preprocessing step in problems of image processing and computer vision. Its performance has a great influence on subsequent tasks. Evolutionary Computation (EC) techniques have been introduced to the area of image segmentation due to their high search capacity. However, there are rarely comprehensive surveys on EC based image segmentation methods, which can enable researchers to get a quick understanding of this area and compare the existing methods. Therefore, this paper provides an overview of EC based image segmentation methods, and discusses the remaining issues in this area. It is observed that among all EC techniques, four of them (genetic algorithms, genetic programming, differential equation and partial swarm optimization) are more frequently used and GAs are the most popular technique. It is noted that low generalization capacity and computational complexity are two common problems in EC techniques applied to image segmentation.",

}

Genetic Programming entries for Yuyu Liang Mengjie Zhang Will N Browne

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