Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/109074
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dc.contributor.author | Neumann, A. | - |
dc.contributor.author | Szpak, Z.L. | - |
dc.contributor.author | Chojnacki, W. | - |
dc.contributor.author | Neumann, F. | - |
dc.contributor.editor | Bosman, P.A.N. | - |
dc.date.issued | 2017 | - |
dc.identifier.citation | Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2017), 2017 / Bosman, P.A.N. (ed./s), vol.-, pp.817-824 | - |
dc.identifier.isbn | 9781450349208 | - |
dc.identifier.uri | http://hdl.handle.net/2440/109074 | - |
dc.description.abstract | Evolutionary algorithms have recently been used to create a wide range of artistic work. In this paper, we propose a new approach for the composition of new images from existing ones, that retain some salient features of the original images. We introduce evolutionary algorithms that create new images based on a fitness function that incorporates feature covariance matrices associated with different parts of the images. This approach is very flexible in that it can work with a wide range of features and enables targeting specific regions in the images. For the creation of the new images, we propose a population-based evolutionary algorithm with mutation and crossover operators based on random walks. Our experimental results reveal a spectrum of aesthetically pleasing images that can be obtained with the aid of our evolutionary process. | - |
dc.description.statementofresponsibility | Aneta Neumann, Zygmunt L. Szpak, Wojciech Chojnacki, Frank Neumann | - |
dc.language.iso | en | - |
dc.publisher | Association for Computing Machinery | - |
dc.rights | © 2017 Copyright held by the owner/author(s). Publication rights licensed to ACM. | - |
dc.source.uri | https://dl.acm.org/doi/proceedings/10.1145/3071178 | - |
dc.subject | Evolutionary algorithms; features; covariance matrices; image composition; digital art | - |
dc.title | Evolutionary image composition using feature covariance matrices | - |
dc.type | Conference paper | - |
dc.contributor.conference | Genetic and Evolutionary Computation Conference (GECCO) (15 Jul 2017 - 19 Jul 2017 : Berlin, Germany) | - |
dc.identifier.doi | 10.1145/3071178.3071260 | - |
dc.publisher.place | New York, NY | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/DP140103400 | - |
dc.relation.grant | http://purl.org/au-research/grants/arc/LE160100090 | - |
pubs.publication-status | Published | - |
dc.identifier.orcid | Neumann, A. [0000-0002-0036-4782] | - |
Appears in Collections: | Aurora harvest 7 Computer Science publications |
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