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Pattern Analysis and Applications
Erschienen in: Pattern Analysis and Applications 1/2017

31.01.2015 | Theoretical Advances

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

verfasst von: Paulo S. Rodrigues, Guilherme A. Wachs-Lopes, Horst R. Erdmann, Monael P. Ribeiro, Gilson A. Giraldi

Erschienen in: Pattern Analysis and Applications | Ausgabe 1/2017

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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 cross-entropy presented 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.
Nächster Artikel Cluster validity index based on Jeffrey divergence

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Fußnoten
1
We define the composed distribution, also called direct product of \(P=(p_{1},\ldots ,p_{n})\) and \(Q=(q_{1},\ldots ,q_{m})\), as \(P*Q=\{p_{i}q_{j}\}_{i,j}\), with \(1\le i\le n\) and \(1\le j\le m\)
 
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Metadaten
Titel
Improving a firefly meta-heuristic for multilevel image segmentation using Tsallis entropy
verfasst von
Paulo S. Rodrigues
Guilherme A. Wachs-Lopes
Horst R. Erdmann
Monael P. Ribeiro
Gilson A. Giraldi
Publikationsdatum
31.01.2015
Verlag
Springer London
Erschienen in
Pattern Analysis and Applications / Ausgabe 1/2017
Print ISSN: 1433-7541
Elektronische ISSN: 1433-755X
DOI
https://doi.org/10.1007/s10044-015-0450-x

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