Improving a firefly meta-heuristic for multilevel image segmentation using Tsallis entropy
N/D
Tipo de produção
Artigo
Data de publicação
2015
Texto completo (DOI)
Periódico
Pattern Analysis and Applications (Print)
Editor
Texto completo na Scopus
Citações na Scopus
Autores
RODRIGUES, Paulo
LOPES, Guilherme
ERDMANN, H. R.
RIBEIRO, M. P.
GIRALDI, G. A.
Orientadores
Resumo
In 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
Citação
RODRIGUES, 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.
Palavras-chave
Keywords
Firefly meta-heuristic; Tsallis entropy; Image segmentation; Optimization