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dc.contributor.authorSalimi, Elham
dc.date.accessioned2016-11-10T18:12:02Z
dc.date.available2016-11-10T18:12:02Z
dc.identifier.urihttp://hdl.handle.net/10464/10707
dc.description.abstractThis thesis is focused on using genetic programming to evolve images based on lightweight features extracted from a given target image. The main motivation of this thesis is research by Lombardi et al. in which an image retrieval system is developed based on lightweight statistical features of images for comparing and classifying them in painting style categories; primarily based on color matching. In this thesis, automatic fitness scoring of variations of up to 17 lightweight image features using many-objective fitness evaluation was used to evolve textures. Evolved results were shown to have similar color characteristics to target images. Although a human survey was conducted to confirm those results, it was inconclusive.en_US
dc.language.isoengen_US
dc.publisherBrock Universityen_US
dc.subjectImage analysis, Image evolution, Evolutionary arts, Statistical analysis, Lightweight image retrievalen_US
dc.titleStatistical Image Analysis for Image Evolutionen_US
dc.typeElectronic Thesis or Dissertationen_US
dc.degree.nameM.Sc. Computer Scienceen_US
dc.degree.levelMastersen_US
dc.contributor.departmentDepartment of Computer Scienceen_US
dc.degree.disciplineFaculty of Mathematics and Scienceen_US
refterms.dateFOA2021-08-04T03:40:33Z


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