dc.contributor.author | Ripon, Kazi Shah Nawaz | |
dc.contributor.author | Ali, Lasker Ershad | |
dc.contributor.author | Newaz, Sarfaraz | |
dc.contributor.author | Ma, Jinwen | |
dc.date.accessioned | 2018-04-18T08:08:02Z | |
dc.date.available | 2018-04-18T08:08:02Z | |
dc.date.created | 2018-02-13T15:42:02Z | |
dc.date.issued | 2017 | |
dc.identifier.citation | Lecture Notes in Computer Science. 2017, 10682 LNAI 168-177. | nb_NO |
dc.identifier.issn | 0302-9743 | |
dc.identifier.uri | http://hdl.handle.net/11250/2494597 | |
dc.description.abstract | In this paper, we present a multi-objective segmentation approach for color images. Three objectives, overall deviation, edge value, and connectivity measure, are optimized simultaneously using a multi-objective evolutionary algorithm (MOEA). To demonstrate the effectiveness of the proposed approach, experiments are conducted on benchmark images. The results justify that the proposed approach is able to partition color images in a number of segments consistent with human visual perception. For quantitative evaluation, we extend the existing Probabilistic Rand Index (PRI) considering multi-objective segmentation. The outcomes show that the proposed approach can obtain non-dominated and near-optimal segment solutions satisfying several criteria simultaneously. It can also find the correct number of segments automatically. | nb_NO |
dc.language.iso | eng | nb_NO |
dc.publisher | Springer Verlag | nb_NO |
dc.title | A multi-objective evolutionary algorithm for color image segmentation | nb_NO |
dc.type | Journal article | nb_NO |
dc.type | Peer reviewed | nb_NO |
dc.description.version | acceptedVersion | nb_NO |
dc.source.pagenumber | 168-177 | nb_NO |
dc.source.volume | 10682 LNAI | nb_NO |
dc.source.journal | Lecture Notes in Computer Science | nb_NO |
dc.identifier.doi | 10.1007/978-3-319-71928-3_17 | |
dc.identifier.cristin | 1564851 | |
dc.description.localcode | This is a post-peer-review, pre-copyedit version of an article published in [Lecture Notes in Computer Science] Locked until 28.11.2018 due to copyright restrictions. The final authenticated version is available online at: https://link.springer.com/chapter/10.1007%2F978-3-319-71928-3_17 | nb_NO |
cristin.unitcode | 194,63,10,0 | |
cristin.unitname | Institutt for datateknologi og informatikk | |
cristin.ispublished | true | |
cristin.fulltext | original | |
cristin.qualitycode | 1 | |