A fast technique for gray level image thresholding and quantization based on the entropy maximization

Benzid, Redha; Arar, Djemaï; Bentoumi, Miloud

doi:10.1109/ssd.2008.4632831

Cited by 11 publications

(3 citation statements)

References 2 publications

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“…On the other hand, non-parametric techniques yield results with better accuracy and less computational cost 104 . Such techniques calculate threshold values by measurement of entropy 105 , error rate, between-class variance 106 , local maxima, and so on. The most preferred method for thresholding-based image segmentation is the OTSU method 106 .…”

Section: Multi-level Thresholding For Image Segmentationmentioning

confidence: 99%

A hybrid swarm intelligence algorithm for region-based image fusion

Salgotra,

Lamba,

Talwar

et al. 2024

Sci Rep

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This paper proposes a novel multi-hybrid algorithm named DHPN, using the best-known properties of dwarf mongoose algorithm (DMA), honey badger algorithm (HBA), prairie dog optimizer (PDO), cuckoo search (CS), grey wolf optimizer (GWO) and naked mole rat algorithm (NMRA). It follows an iterative division for extensive exploration and incorporates major parametric enhancements for improved exploitation operation. To counter the local optima problems, a stagnation phase using CS and GWO is added. Six new inertia weight operators have been analyzed to adapt algorithmic parameters, and the best combination of these parameters has been found. An analysis of the suitability of DHPN towards population variations and higher dimensions has been performed. For performance evaluation, the CEC 2005 and CEC 2019 benchmark data sets have been used. A comparison has been performed with differential evolution with active archive (JADE), self-adaptive DE (SaDE), success history based DE (SHADE), LSHADE-SPACMA, extended GWO (GWO-E), jDE100, and others. The DHPN algorithm is also used to solve the image fusion problem for four fusion quality metrics, namely, edge-based similarity index ($$Q^{AB/F}$$ Q A B / F ), sum of correlation difference (SCD), structural similarity index measure (SSIM), and artifact measure ($$N^{AB/F}$$ N A B / F ). The average $$Q^{AB/F} = 0.765508$$ Q A B / F = 0.765508 , $$SCD = 1.63185$$ S C D = 1.63185 , $$SSIM = 0.726317$$ S S I M = 0.726317 , and $$N^{AB/F} = 0.006617$$ N A B / F = 0.006617 shows the best combination of results obtained by DHPN with respect to the existing algorithms such as DCH, CBF, GTF, JSR and others. Experimental and statistical Wilcoxon’s and Friedman’s tests show that the proposed DHPN algorithm performs significantly better in comparison to the other algorithms under test.

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Section: Multi-level Thresholding For Image Segmentationmentioning

confidence: 99%

A hybrid swarm intelligence algorithm for region-based image fusion

Salgotra,

Lamba,

Talwar

et al. 2024

Sci Rep

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“…A nonparametric multilevel image segmentation algorithm has been developed by Otsu [6]. Many entropy based algorithm has also been proposed in the recent years including the Shannon entropy [7], Tsalli's entropy [8], Renyi's entropy [9] and a fuzzy entropy based algorithm [10] which effectively contributed to the advancement in the field of Image Segmentation. But the demerits, like longer computational time and complexity, could not be avoided.…”

Section: Introductionmentioning

confidence: 99%

A Type–II Fuzzy Entropy Based Multi-Level Image Thresholding Using Adaptive Plant Propagation Algorithm

Nag¹

2017

Preprint

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One of the most straightforward, direct and efficient approaches to Image Segmentation is Image Thresholding. Multi-level Image Thresholding is an essential viewpoint in many image processing and Pattern Recognition based real-time applications which can effectively and efficiently classify the pixels into various groups denoting multiple regions in an Image. Thresholding based Image Segmentation using fuzzy entropy combined with intelligent optimization approaches are commonly used direct methods to properly identify the thresholds so that they can be used to segment an Image accurately. In this paper a novel approach for multi-level image thresholding is proposed using Type II Fuzzy sets combined with Adaptive Plant Propagation Algorithm (APPA). Obtaining the optimal thresholds for an image by maximizing the entropy is extremely tedious and time consuming with increase in the number of thresholds. Hence, Adaptive Plant Propagation Algorithm (APPA), a memetic algorithm based on plant intelligence, is used for fast and efficient selection of optimal thresholds. This fact is reasonably justified by comparing the accuracy of the outcomes and computational time consumed by other modern state-of-the-art algorithms such as Particle Swarm Optimization (PSO), Gravitational Search Algorithm (GSA) and Genetic Algorithm (GA).

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“…Among global thresholding techniques, entropy based methods like Shannon entropy [3,25], Renyi entropy [22], Tsalli entropy [23].…”

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confidence: 99%

Rough-Fuzzy Collaborative Multi-level Image Thresholding: A Differential Evolution Approach

Shrivastava

Datta

Das

2015

Advances in Intelligent Systems and Computing

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In this article, a granular computing based multi-level gray image thresholding algorithm is presented. An image is divided into spatial blocks called granules, and the classes of gray levels are represented using a fuzzy-rough collaborative approach, where the measure of roughness of a rough set is also modified from the classical definition of rough sets. This measure for each rough set is minimized simultaneously to obtain the optimal thresholds. Tchebycheff decomposition approach is employed to transform this multi-objective optimization problem to a single objective optimization problem. Differential Evolution (DE), one of the most efficient evolutionary optimizers of current interest, is used to optimize this single objective function, thus reducing the execution time. Superiority of the proposed method is presented by comparing it with some popular image thresholding techniques. MSSIM index and Probabilistic Rand Index (PRI) are used for quantitative comparison on the Berkley Image Segmentation Data Set (BSDS300).

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A fast technique for gray level image thresholding and quantization based on the entropy maximization

Cited by 11 publications

References 2 publications

A hybrid swarm intelligence algorithm for region-based image fusion

A hybrid swarm intelligence algorithm for region-based image fusion

A Type–II Fuzzy Entropy Based Multi-Level Image Thresholding Using Adaptive Plant Propagation Algorithm

Rough-Fuzzy Collaborative Multi-level Image Thresholding: A Differential Evolution Approach

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