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

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• 作者：Paulo S. Rodrigues ; Guilherme A. Wachs-Lopes…
• 关键词：Firefly meta ; heuristic ; Tsallis entropy ; Image segmentation ; Optimization
• 刊名：Pattern Analysis and Applications
• 出版年：2017
• 出版时间：February 2017
• 年：2017
• 卷：20
• 期：1
• 页码：1-20
• 全文大小：
• 刊物类别：Computer Science
• 刊物主题：Pattern Recognition;
• 出版者：Springer London
• ISSN：1433-755X
• 卷排序：20

文摘

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.