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A multilevel color image thresholding scheme based on minimum cross entropy and differential evolution

PATTERN RECOGNITION LETTERS 2015年第Mar.1期54卷 27-35页

作者： Sarkar, Soham Das, Swagatam Chaudhuri, Sheli Sinha RCC Inst Informat Technol Dept Elect & Commun Engn Kolkata 700015 India Indian Stat Inst Elect & Commun Sci Unit Kolkata 700108 India Jadavpur Univ Dept Elect & Telecommun Engn Kolkata 700032 India

We propose a novel multi-level thresholding method for unsupervised separation between objects and background from a natural color image using the concept of the minimum cross entropy (MCE). MCE based thresholding techniques are widely popular for segmenting grayscale images. Color image segmentation is still a challenging field as it involves 3-D histogram unlike the 1-D histogram of grayscale images. Effectiveness of entropy based multi-level thresholding for color image is yet to be explored and this paper presents a humble contribution in this context. We have used differential evolution (DE), a simple yet efficient evolutionary algorithm of current interest, to improve the computation time and robustness of the proposed algorithm. The performance of DE is also investigated extensively through comparison with other well-known nature inspired global optimization techniques like genetic algorithm (GA), particle swarm optimization (PSO), and artificial bee colony (ABC). The proposed method is evaluated by comparing it with seven other prominent algorithms both qualitatively and quantitatively using a well known benchmark suite - the Barkley segmentation dataset (BSDS300) with 300 distinct images. Such comparison reflects the efficiency of our algorithm (C) 2014 Published by Elsevier B.V.

关键词： Multi-Level image segmentation Color image Minimum cross entropy thresholding Differential evolution berkeley segmentation dataset and benchmark

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Multilevel Image Thresholding Based on 2D Histogram and Maximum Tsallis Entropy-A Differential Evolution Approach

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2013年第12期22卷 4788-4797页

作者： Sarkar, Soham Das, Swagatam RCC Inst Informat Technol Kolkata Elect & Commun Engn Dept Kolkata 700015 India Indian Stat Inst Elect & Commun Sci Unit Kolkata 700108 India

Multilevel thresholding amounts to segmenting a gray-level image into several distinct regions. This paper presents a 2D histogram based multilevel thresholding approach to improve the separation between objects. Recent studies indicate that the results obtained with 2D histogram oriented approaches are superior to those obtained with 1D histogram based techniques in the context of bi-level thresholding. Here, a method to incorporate 2D histogram related information for generalized multilevel thresholding is proposed using the maximum Tsallis entropy. Differential evolution (DE), a simple yet efficient evolutionary algorithm of current interest, is employed to improve the computational efficiency of the proposed method. The performance of DE is investigated extensively through comparison with other well-known nature inspired global optimization techniques such as genetic algorithm, particle swarm optimization, artificial bee colony, and simulated annealing. In addition, the outcome of the proposed method is evaluated using a well known benchmark-the Berkley segmentation data set (BSDS300) with 300 distinct images.

关键词： Multilevel image segmentation 2D histogram Tsallis entropy thresholding differential evolution berkeley segmentation dataset and benchmark

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