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|Title:||Improving a firefly meta-heuristic for multilevel image segmentation using Tsallis entropy|
ERDMANN, H. R.
RIBEIRO, M. P.
GIRALDI, G. A.
|Abstract:||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|
|Journal:||Pattern Analysis and Applications (Print)|
|Citation:||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.|
|Access Type:||Acesso Aberto|
|Appears in Collections:||Artigos|
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