{"id":{"repo_id":"brock","oai_identifier":"oai:brocku.scholaris.ca:10464/10707"},"canonical_url":"https://search.dev.ndltd.org/etd/brock/oai:brocku.scholaris.ca:10464/10707","repository":{"repo_id":"brock","name":"Brock University","base_url":"https://brocku.scholaris.ca/server/oai/request"},"display":{"title":"Statistical Image Analysis for Image Evolution","abstract":"This 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. 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Although a human survey was conducted to confirm those results, it was inconclusive."]},{"key":"dc:title","label":"Title","values":["Statistical Image Analysis for Image Evolution"]}]}],"canonical_facts":{"dc:contributor.department":["Department of Computer Science"],"dc:creator":["Salimi, Elham"],"dc:date.accessioned":["2016-11-10T18:12:02Z"],"dc:date.available":["2016-11-10T18:12:02Z"],"dc:date.issued":["2016-11-10T18:12:02Z"],"dc:description.abstract":["This 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. 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