Color image quantization using the shuffled-frog leaping algorithm

Delgado, María Luisa Pérez

doi:10.1016/j.engappai.2019.01.002

Cited by 38 publications

(23 citation statements)

References 48 publications

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“…In 2019, the application of the shuffled-frog leaping algorithm to perform color quantization was proposed by Pérez-Delgado [11], and this method is identified as SFLA-CQ. The SFLA-CQ algorithm was coded in C language and executed on a PC running under the Linux operating system with 8 GBytes of RAM and an AMD Ryzen 7 1800X Turbo processor (4.0 GHz).…”

Section: Of 14mentioning

confidence: 99%

“…In their method, a combination of linear discriminant analysis and principal analysis was used to accomplish effective partitioning of the color space. In 2019, application of the shuffled-frog leaping algorithm to perform color image quantization was proposed by Pérez-Delgado [11]. Each frog represented a quantized palette and the objective function considered is the mean square error.…”

Section: Introductionmentioning

confidence: 99%

“…Results of the method proposed by Pérez-Delgado[11]. (Sr: sampling rate, MSE a : average MSE, T: average computation time (milliseconds)).…”

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confidence: 99%

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Color Image Quantization Based on the Artificial Bee Colony and Accelerated K-means Algorithms

Huang

2020

Symmetry

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Color image quantization techniques have been widely used as an important approach in color image processing and data compression. The key to color image quantization is a good color palette. A new method for color image quantization is proposed in this study. The method consists of three stages. The first stage is to generate N colors based on 3D histogram computation, the second is to obtain the initial palette by selecting K colors from the N colors based on an artificial bee colony algorithm, and the third is to obtain the quantized images using the accelerated K-means algorithm. In order to reduce the computation time, the sampling process is employed. The closest color in the palette for each sampled color pixel in the color image is efficiently determined by the mean-distance-ordered partial codebook search algorithm. The experimental results show that the proposed method can generate high-quality quantized images with less time consumption.

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Section: Of 14mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Color Image Quantization Based on the Artificial Bee Colony and Accelerated K-means Algorithms

Huang

2020

Symmetry

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“…Jiang et al [16] presented a Cauchy oscillation SFLA for updating the premise parameters of the adaptive neuro-fuzzy inference system to improve the learning ability and prediction accuracy of the algorithm for electrical resistivity imaging inversion. Pérez-Delgado [17] propsed a SFLA for color image quantization. Computational results indicate that the method can generate a quantized image with low computational cost.…”

Section: ) Hybridizingmentioning

confidence: 99%

Lévy Flight Shuffle Frog Leaping Algorithm Based on Differential Perturbation and Quasi-Newton Search

Zhang

Chen

et al. 2019

IEEE Access

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Lévy flight Shuffle Frog Leaping Algorithm (LSFLA) is a SFLA variant and enhances the performance of SFLA largely, however, it still has some defects, such as poor convergence and low efficiency. So an improved LSFLA, namely, LSFLA based on Differential perturbation and Quasi-Newton search (DQLSFLA), is proposed in this paper. Firstly, the way of updating only one solution which is the worst one at every sub-iteration in LSFLA is replaced with an all-solution updating way in the subgroup to improve the probability of obtaining the best solution, to omit one sorting step at every sub-iteration and the subiteration number parameter setting, to reduce the computational complexity and to enhance the optimization efficiency. Secondly, a random differential perturbation approach and an improved Lévy flight updating one are created and used in the subgroup updating to form DLSFLA, that is, the first half frogs in the subgroup use the random differential perturbation updating approach to improve the global search ability, and the last half adopt the improved Lévy flight updating method to keep the advantage of the Lévy flight updating method and overcome its defects. Finally, quasi-Newton local search is integrated into DLSFLA near the end of the algorithm to improve the convergence speed. Experimental results on the complex functions from CEC-2014 show that DQLSFLA's convergence quality and optimization efficiency are much better than LSFLA's and that compared with quite a few other state-of-the-art algorithms, DQLSFLA has better performance. The results on medical image enhancement and Quadratic Assignment Problem (QAP) also show that DQLSFLA can solve the real word optimization problems better than LSFLA can. INDEX TERMS Intelligent optimization algorithm, shuffled frog leaping algorithm (SFLA), Lévy flight, quasi-Newton search, image enhancement, quadratic assignment problem.

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“…Another group of algorithms based on this approach are those that use a swarm of individuals to solve a complex problem. Some methods of this type applied to color quantization are the Particle swarm optimization algorithm [37,38], the Ant-tree for color quantization (ATCQ) method [39], the Iterative ant-tree for color quantization (ITATCQ) method [40], the Artificial bee colony algorithm combined with K-means [41], the Artificial bee colony algorithm combined with ATCQ [42], the Firefly algorithm combined with ATCQ [43] and the Shuffled-frog leaping algorithm [44]. Other swarm-based methods applied to color quantization are summarized in [45].…”

Section: Introductionmentioning

confidence: 99%

A Mixed Method with Effective Color Reduction

Delgado

2020

Applied Sciences

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This article presents a color quantization technique that combines two previously proposed approaches: the Binary splitting method and the Iterative ant-tree for color quantization method. The resulting algorithm can obtain good quality images with low time consumption. In addition, the iterative nature of the proposed method allows the quality of the quantized image to improve as the iterations progress, although it also allows a good initial image to be quickly obtained. The proposed method was compared to 13 other color quantization techniques and the results showed that it could generate better quantized images than most of the techniques assessed. The statistical significance of the improvement obtained using the new method is confirmed by applying a statistical test to the results of all the methods compared.

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Color image quantization using the shuffled-frog leaping algorithm

Cited by 38 publications

References 48 publications

Color Image Quantization Based on the Artificial Bee Colony and Accelerated K-means Algorithms

Color Image Quantization Based on the Artificial Bee Colony and Accelerated K-means Algorithms

Lévy Flight Shuffle Frog Leaping Algorithm Based on Differential Perturbation and Quasi-Newton Search

A Mixed Method with Effective Color Reduction

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