RODRIGUES, PauloLOPES, GuilhermeERDMANN, H. R.RIBEIRO, M. P.GIRALDI, G. A.2019-08-172019-08-172015RODRIGUES, 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, 2015.1433-7541https://repositorio.fei.edu.br/handle/FEI/1008In 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 minimumAcesso AbertoArtigo10.1007/s10044-015-0450-xhttps://link.springer.com/content/pdf/10.1007%2Fs10044-015-0450-x.pdfFirefly meta-heuristicTsallis entropyImage segmentationOptimization