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

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Tipo de produção

Artigo

Data de publicação

2015

Texto completo (DOI)

Periódico

Pattern Analysis and Applications (Print)

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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.

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Keywords

Firefly meta-heuristic; Tsallis entropy; Image segmentation; Optimization

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