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.

scholarly journals A Task Learning Mechanism for the Telerobots

2019 ◽  
Vol 16 (02) ◽  
pp. 1950009 ◽  
Author(s):  
Jing Luo ◽  
Chenguang Yang ◽  
Qiang Li ◽  
Min Wang
Keyword(s):  
Gaussian Mixture Model ◽  
Mixture Model ◽  
Gaussian Mixture ◽  
Experimental Results ◽  
Complex Tasks ◽  
Learning Mechanism ◽  
Learning Framework ◽  
Task Learning

Telerobotic systems have attracted growing attention because of their superiority in the dangerous or unknown interaction tasks. It is very challenging to exploit such systems to implement complex tasks in an autonomous way. In this paper, we propose a task learning framework to represent the manipulation skill demonstrated by a remotely controlled robot. Gaussian mixture model is utilized to encode and parametrize the smooth task trajectory according to the observations from the demonstrations. After encoding the demonstrated trajectory, a new task trajectory is generated based on the variability information of the learned model. Experimental results have demonstrated the feasibility of the proposed method.

scholarly journals Fast Color Quantization by K-Means Clustering Combined with Image Sampling

Symmetry ◽  
2019 ◽  
Vol 11 (8) ◽  
pp. 963
Author(s):  
Mariusz Frackiewicz ◽  
Aron Mandrella ◽  
Henryk Palus
Keyword(s):  
Nearest Neighbor ◽  
Color Image ◽  
Assessment Process ◽  
Color Image Processing ◽  
Color Quantization ◽  
Quantization Method ◽  
Quality Indices ◽  
Limited Sample ◽  
High Quality ◽  
Image Quantization

Color image quantization has become an important operation often used in tasks of color image processing. There is a need for quantization methods that are fast and at the same time generating high quality quantized images. This paper presents such color quantization method based on downsampling of original image and K-Means clustering on a downsampled image. The nearest neighbor interpolation was used in the downsampling process and Wu’s algorithm was applied for deterministic initialization of K-Means. Comparisons with other methods based on a limited sample of pixels (coreset-based algorithm) showed an advantage of the proposed method. This method significantly accelerated the color quantization without noticeable loss of image quality. The experimental results obtained on 24 color images from the Kodak image dataset demonstrated the advantages of the proposed method. Three quality indices (MSE, DSCSI and HPSI) were used in the assessment process.

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