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

Symmetry ◽  
2020 ◽  
Vol 12 (8) ◽  
pp. 1222
Author(s):  
Shu-Chien Huang
Keyword(s):  
Artificial Bee Colony ◽  
Color Image ◽  
Search Algorithm ◽  
Computation Time ◽  
Color Palette ◽  
Bee Colony ◽  
Image Quantization ◽  
Important Approach ◽  
Three Stages ◽  
Color Pixel

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.

Global Artificial Bee Colony Search Algorithm for Data Clustering

2019 ◽  
Vol 10 (2) ◽  
pp. 48-59
Author(s):  
Zeeshan Danish ◽  
Habib Shah ◽  
Nasser Tairan ◽  
Rozaida Gazali ◽  
Akhtar Badshah
Keyword(s):  
Data Mining ◽  
Vector Quantization ◽  
Data Clustering ◽  
Artificial Bee Colony ◽  
Search Algorithm ◽  
Computation Time ◽  
Search Space ◽  
Data Mining Technique ◽  
Mining Technique ◽  
Bee Colony

Data clustering is a widespread data compression, vector quantization, data analysis, and data mining technique. In this work, a modified form of ABC, i.e. global artificial bee colony search algorithm (GABCS) is applied to data clustering. In GABCS the modification is due to the fact that experienced bees can use past information of quantity of food and position to adjust their movements in a search space. Due to this fact, solution search equations of the canonical ABC are modified in GABCS and applied to three famous real datasets in this work i.e. iris, thyroid, wine, accessed from the UCI database for the purpose of data clustering and results were compared with few other stated algorithms such as K-NM-PSO, TS, ACO, GA, SA and ABC. The results show that while calculating intra-clustering distances and computation time on all three real datasets, the proposed GABCS algorithm gives far better performance than other algorithms whereas calculating computation numbers it performs adequately as compared to typical ABC.

scholarly journals An Efficient Palette Generation Method for Color Image Quantization

2021 ◽  
Vol 11 (3) ◽  
pp. 1043
Author(s):  
Shu-Chien Huang
Keyword(s):  
Color Image ◽  
Sampling Rate ◽  
Color Space ◽  
Computation Time ◽  
Input Image ◽  
Blue Color ◽  
Color Palette ◽  
Image Quantization ◽  
Searching Method ◽  
Two Stages

This article describes an efficient method to generate a color palette for color image quantization. The method consists of two stages. In the first stage, the initial palette is generated. Initially, the color palette is an empty set. First, the N colors are generated according to the data distribution of the input image in the RGB (Red, Green, Blue) color space. Then, one color is selected from the N colors and this color is added to the initial palette, and the step is repeated until the color number of the initial palette is equal to K. In the second stage, the quantized image is generated using the fast K-means algorithm. There are many sampling rates used in this study. For each sampled pixel, a fast searching method is employed to efficiently determine the closest color in the palette. Experimental results show that the high-quality quantized images can be generated by the proposed method. When the sampling rate equals 0.125, the computation time of the proposed method is less than 0.3 s for all cases.

Color Quantization Based on Gaussian Mixture Mode

2012 ◽  
Vol 457-458 ◽  
pp. 650-654
Author(s):  
Qiu Chun Jin ◽  
Xiao Li Tong
Keyword(s):  
Image Analysis ◽  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Color Image ◽  
Gaussian Mixture ◽  
Experimental Results ◽  
Color Quantization ◽  
Quantization Method ◽  
Color Palette ◽  
Image Quantization

Color quantization is an important technique for image analysis that reduces the number of distinct colors for a color image. A novel color image quantization algorithm based on Gaussian mixture model is proposed. In the approach, we develop a Gaussian mixture model to design the color palette. Each component in the GMM represents a type of color in the color palette. The task of color quantization is to group pixels into different component. Experimental results show that our quantization method can obtain better results than other methods.

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