A genetic programming framework in the automatic design of combination models for salient object detection

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@Article{Contreras-Cruz:GPEM,

• author = "Marco A. Contreras-Cruz and Diana E. Martinez-Rodriguez and Uriel H. Hernandez-Belmonte and Victor Ayala-Ramirez",
• title = "A genetic programming framework in the automatic design of combination models for salient object detection",
• journal = "Genetic Programming and Evolvable Machines",
• year = "2019",
• volume = "20",
• number = "3",
• pages = "285--325",
• month = sep,
• keywords = "genetic algorithms, genetic programming, Visual attention, Contrast based methods, Evolutionary computation, Fusion strategies, Saliency enhancement, Saliency aggregation",
• ISSN = "1389-2576",
• DOI = "doi:10.1007/s10710-019-09345-5",
• size = "41 pages",
• abstract = "In computer vision, the salient object detection problem consists of finding the most attention-grabbing objects in images. In the last years, many researchers have proposed salient object detection algorithms to address this problem. However, most of the algorithms perform well only on images with specific conditions and they do not solve the general problem. To cope with a more significant number of image types than those where each standalone saliency detection method performs well, novel methods search to generate a combination model that improves the overall performance of detecting salient objects in images. The contribution of this work is oriented towards the automatic design of combination models by using genetic programming. The proposed approach automatically selects the algorithms to be combined and the combination operators that result in an improvement in the overall performance. The evolutionary approach uses as input a set of candidate saliency detection methods ...",
• notes = "Electronics Engineering Department, Universidad de Guanajuato, Mexico",

}

Genetic Programming entries for Marco Antonio Contreras Cruz Diana E Martinez-Rodriguez Uriel Haile Hernandez Belmonte Victor Ayala-Ramirez

Citations