Improving a firefly meta-heuristic for multilevel image segmentation using Tsallis entropy

dc.creatorRODRIGUES, Paulo
dc.creatorLOPES, Guilherme
dc.creatorERDMANN, H. R.
dc.creatorRIBEIRO, M. P.
dc.creatorGIRALDI, G. A.
dc.date.accessioned2019-08-17T20:00:30Z
dc.date.accessioned2022-09-21T19:50:17Z
dc.date.accessioned2023-03-13T21:04:02Z
dc.date.available2019-08-17T20:00:30Z
dc.date.available2022-09-21T19:50:17Z
dc.date.available2023-03-13T21:04:02Z
dc.date.created2019-08-17T20:00:30Z
dc.date.created2022-09-21T19:50:17Z
dc.date.issued2015
dc.identifierRODRIGUES, Paulo; LOPES, Guilherme; ERDMANN, H. R.; RIBEIRO, M. P.; GIRALDI, G. A. Improving a firefly meta-heuristic for multilevel image segmentation using Tsallis entropy. Pattern Analysis and Applications (Print), v. 1, p. 1-20, 2015.
dc.identifier1433-7541
dc.identifierhttps://hdl.handle.net/20.500.12032/40540
dc.identifier.urihttps://repositorioslatinoamericanos.uchile.cl/handle/2250/6178649
dc.description.abstractIn this paper we show that the non-extensive Tsallis entropy, when used as kernel in the bio-inspired firefly algorithm for multi-thresholding in image segmentation, is more efficient than using the traditional crossentropy resented in the literature. The firefly algorithm is a swarm-based meta-heuristic, inspired by fireflies-seeking behavior following their luminescence. We show that the use of more convex kernels, as those based on non-extensive entropy, is more effective at 5 % of significance level than the cross-entropy counterpart when applied in synthetic spaces for searching thresholds in global minimum
dc.relationPattern Analysis and Applications (Print)
dc.rightsAcesso Aberto
dc.titleImproving a firefly meta-heuristic for multilevel image segmentation using Tsallis entropy
dc.typeArtigo


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