Tsallis entropy based optimal multilevel thresholding using cuckoo search algorithm

被引：178

作者：

Agrawal, Sanjay ^{[1
]}

Panda, Rutuparna ^{[1
]}

Bhuyan, Sudipta ^{[1
]}

Panigrahi, B. K. ^{[2
]}

机构：

[1] Veer Surendra Sai Univ Technol, Dept Elect & Telecommun Engn, Burla 768018, India

[2] IIT Delhi, Dept Elect Engn, New Delhi 110003, India

来源：

SWARM AND EVOLUTIONARY COMPUTATION | 2013年 / 11卷

关键词：

Image segmentation; Multi-level thresholding; Cuckoo search algorithm; Tsallis entropy; MINIMUM; OPTIMIZATION;

D O I：

10.1016/j.swevo.2013.02.001

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

In this paper, optimal thresholds for multi-level thresholding in an image are obtained by maximizing the Tsallis entropy using cuckoo search algorithm. The method is considered as a constrained optimization problem. The solution is obtained through the convergence of a meta-heuristic search algorithm. The proposed algorithm is tested on standard set of images. The results are then compared with that of bacteria foraging optimization (BFO), artificial bee colony (ABC) algorithm, particle swarm optimization (PSO) and genetic algorithm (GA). Results are analyzed both qualitatively and quantitatively. It is observed that our results are also encouraging in terms of CPU time and objective function values. (C) 2013 Elsevier B.V. All rights reserved.

引用

页码：16 / 30

页数：15

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