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.

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.

EXTRACTION OF BIBLIOGRAPHY INFORMATION BASED ON IMAGE OF BOOK COVER

2000 ◽  
Vol 14 (07) ◽  
pp. 963-978 ◽  
Author(s):  
HUA YANG ◽  
MASAAKI KASHIMURA ◽  
NORIKADU ONDA ◽  
SHINJI OZAWA
Keyword(s):  
Color Image ◽  
Color Space ◽  
Input Image ◽  
Color Image Processing ◽  
Front Cover ◽  
Stroke Width ◽  
Other Information ◽  
Region Extraction ◽  
Hsi Color Space ◽  
Rgb Color Space

This paper describes a new system for extracting and classifying bibliography regions from the color image of a book cover. The system consists of three major components: preprocessing, color space segmentation and text region extraction and classification. Preprocessing extracts the edge lines of the book and geometrically corrects and segments the input image, into the parts of front cover, spine and back cover. The same as all color image processing researches, the segmentation of color space is an essential and important step here. Instead of RGB color space, HSI color space is used in this system. The color space is segmented into achromatic and chromatic regions first; and both the achromatic and chromatic regions are segmented further to complete the color space segmentation. Then text region extraction and classification follow. After detecting fundamental features (stroke width and local label width) text regions are determined. By comparing the text regions on front cover with those on spine, all extracted text regions are classified into suitable bibliography categories: author, title, publisher and other information, without applying OCR.

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.

Image Segmentation Utilizing Color-Space Feature

2015 ◽  
Vol 6 (1) ◽  
pp. 39-53 ◽  
Author(s):  
Mohammad A. Al-Jarrah
Keyword(s):  
Image Segmentation ◽  
Color Image ◽  
Color Space ◽  
Color Image Segmentation ◽  
Blue Color ◽  
Space Correlation ◽  
Gray Image ◽  
Segmentation Methods ◽  
Third Stage ◽  
Thresholding Algorithm

In this paper, the authors introduced a stochastic model for color images. Utilizing this model, they proposed a new method for color image segmentation. The proposed method consists of three stages; the first stage considers the red, green, and blue color component of the image as a gray image. One of the known gray image Thresholding algorithm is applied on the three color components. The second stage segments the image based on the results of first stage. This stage produces eight color segments. The third stage identifies the segments through color-space correlation. Color-space correlation algorithm assumes that a set of pixels are considered to belong to one region if and only if they belong to the same color cluster and all connected using neighborhood filters. The last stage may produce very small segments. These small segments are merged with their closed neighbors based on color features. Finally, Conducted experiments achieved perceptually accepted segments and compare favorably to other segmentation methods.

A FAST AND ROBUST ALGORITHM FOR COLOR-BLOB TRACKING IN MULTI-ROBOT COORDINATED TASKS

2006 ◽  
Vol 03 (03) ◽  
pp. 191-200 ◽  
Author(s):  
YING WANG ◽  
CLARENCE W. DE SILVA
Keyword(s):  
Template Matching ◽  
Cooperative Control ◽  
Color Image ◽  
Color Space ◽  
Route Planning ◽  
Response Speed ◽  
Blue Color ◽  
Robust Algorithm ◽  
Blob Tracking ◽  
Multi Robot

In this paper, a fast and robust algorithm is presented for color-blob tracking, which is applicable in a multi-robot cooperative control system. The algorithm, which is immune to uneven illumination, identifies the current poses (positions and orientations) of a robot and the manipulated object (a rectangle box) from a color image, in real time. Two main challenges are faced in the multi-robot task considered in the paper. The first one concerns the response speed of the vision subsystem. The second challenge comes from uneven lighting, which makes it very difficult for the vision subsystem to trace a specific color blob in different positions. A fast computer vision algorithm is presented to cope with these challenges. First, an image in the RGB (Red-Green-Blue) color space is converted into the HSI (Hue-Saturation-Intensity) color space. Then the Saturation and the Intensity components of the image are removed and only the Hue component is retained. Second, filtering and template matching technologies are employed to remove the disturbances from the background and other objects in the image. Finally, coordinate transformations are used to reconstruct the poses of the robot and the object when they are moving. A multi-robot route planning approach is presented, which uses the information acquired by the color-blob tracking algorithm. The experimental results are presented to show the feasibility and the effectiveness of the algorithm.

Underwater Color Image Enhancement Using Combining Schemes

2014 ◽  
Vol 48 (3) ◽  
pp. 57-62 ◽  
Author(s):  
Xin Luan ◽  
Guojia Hou ◽  
Zhengyuan Sun ◽  
Yongfang Wang ◽  
Dalei Song ◽  
...  
Keyword(s):  
Image Enhancement ◽  
Color Image ◽  
Color Space ◽  
Color Contrast ◽  
Blue Color ◽  
Wavelet Domain ◽  
Nonuniform Illumination ◽  
Underwater Image ◽  
Homomorphic Filtering ◽  
Contrast Stretching

AbstractUnderwater color image processing has received considerable attention in the last few decades for underwater image-based observation. In this article, a novel underwater image enhancement approach using combining schemes is presented. This study aims to improve color correction under nonuniform illumination conditions. The objective of this approach is threefold. First, to correct nonuniform illumination and enhance contrast in the image, homomorphic filtering is used. Second, the color contrast of an image is equalized by a contrast stretching algorithm in RGB (red, green and blue) color space. Finally, the noise amplified after the previous two steps is suppressed by using wavelet domain denoising based on threshold processing. The comparison of experimental results shows that the proposed approach of underwater image enhancement can correct the color imbalance and is especially suitable for processing underwater color images that have nonuniform lighting.

scholarly journals Intelligent Bio-Inspired Whale Optimization Algorithm for Color Image based Segmentation

2020 ◽  
Vol 28 (4) ◽  
Author(s):  
Athraa Jasim Mohammed ◽  
Khalil Ibrahim Ghathwan
Keyword(s):  
Optimization Algorithm ◽  
Heuristic Algorithms ◽  
Color Image ◽  
Clustering Algorithms ◽  
Color Space ◽  
Input Image ◽  
Whale Optimization Algorithm ◽  
Color Image Segmentation ◽  
Local Optima ◽  
Whale Optimization

Color image segmentation is widely used methods for searching of homogeneous regions to classify them into various groups. Clustering is one technique that is used for this purpose. Clustering algorithms have drawbacks such as the finding of optimum centers within a cluster and the trapping in local optima. Even though inspired meta-heuristic algorithms have been adopted to enhance the clustering performance, some algorithms still need improvements. Whale optimization algorithm (WOA) is recognized to be enough competition with common meta-heuristic algorithms, where it has an ability to obtain a global optimal solution and avoid local optima. In this paper, a new method for color image based segmentation is proposed based on using whale optimization algorithm in clustering. The proposed method is called the whale color image based segmentation (WhCIbS). It was used to divide the color image into a predefined number of clusters. The input image in RGB color space was converted into L*a*b color space. Comparison of the proposed WhCIbS method was performed with the wolf color image based segmentation, cuckoo color image based segmentation, bat color image based segmentation, and k-means color image based segmentation over four benchmark color images. Experimental results demonstrated that the proposed WhCIbS had higher value of PSNR and lower value of RMSR in most cases compared to other methods.

Selection of small color palette for color image quantization

1992 ◽  
Author(s):  
Wing K. Chau ◽  
S. K. M. Wong ◽  
Xuedong Yang ◽  
Shijie J. Wan
Keyword(s):  
Color Image ◽  
Color Palette ◽  
Image Quantization ◽  
Selection Of

Pencarian File Gambar Berdasarkan Dominasi Warna

2011 ◽  
Vol 2 (1) ◽  
Author(s):  
Vina Chovan Epifania ◽  
Eko Sediyono
Keyword(s):  
Digital Image ◽  
Retrieval System ◽  
Color Image ◽  
Color Space ◽  
Image Data ◽  
Image Search ◽  
Test Results ◽  
Application Form ◽  
Space Model ◽  
Image File

Abstract. Image File Searching Based on Color Domination. One characteristic of an image that can be used in image searching process is the composition of the colors. Color is a trait that is easily seen by man in the picture. The use of color as a searching parameter can provide a solution in an easier searching for images stored in computer memory. Color images have RGB values that can be computed and converted into HSL color space model. Use of HSL images model is very easy because it can be calculated using a percent, so that in each pixel of the image can be grouped and named, this can give a dominant values of the colors contained in one image. By obtaining these values, the image search can be done quickly just by using these values to a retrieval system image file. This article discusses the use of the HSL color space model to facilitate the searching for a digital image in the digital image data warehouse. From the test results of the application form, a searching is faster by using the colors specified by the user. Obstacles encountered were still searching with a choice of 15 basic colors available, with a limit of 33% dominance of the color image search was not found. This is due to the dominant color in each image has the most dominant value below 33%.   Keywords: RGB, HSL, image searching Abstrak. Salah satu ciri gambar yang dapat dipergunakan dalam proses pencarian gambar adalah komposisi warna. Warna adalah ciri yang mudah dilihat oleh manusia dalam citra gambar. Penggunaan warna sebagai parameter pencarian dapat memberikan solusi dalam memudahkan pencarian gambar yang tersimpan dalam memori komputer. Warna gambar memiliki nilai RGB yang dapat dihitung dan dikonversi ke dalam model HSL color space. Penggunaan model gambar HSL sangat mudah karena dapat dihitung dengan menggunakan persen, sehingga dalam setiap piksel gambar dapat dikelompokan dan diberi nama, hal ini dapat memberikan suatu nilai dominan dari warna yang terdapat dalam satu gambar. Dengan diperolehnya nilai tersebut, pencarian gambar dapat dilakukan dengan cepat hanya dengan menggunakan nilai tersebut pada sistem pencarian file gambar. Artikel ini membahas tentang penggunaan model HSL color space untuk mempermudah pencarian suatu gambar digital didalam gudang data gambar digital. Dari hasil uji aplikasi yang sudah dibuat, diperoleh pencarian yang lebih cepat dengan menggunakan pilihan warna yang ditentukan sendiri oleh pengguna. Kendala yang masih dijumpai adalah pencarian dengan pilihan 15 warna dasar yang tersedia, dengan batas dominasi warna 33% tidak ditemukan gambar yang dicari. Hal ini disebabkan warna dominan disetiap gambar kebanyakan memiliki nilai dominan di bawah 33%. Kata Kunci: RGB, HSL, pencarian gambar

Sign in / Sign up

Export Citation Format

Share Document