scholarly journals Preliminary Study on Sapphire Color Grading Method Based on Automatic Clustering Algorithm of Color Space Features

2021 ◽  
Vol 2074 (1) ◽  
pp. 012065
Author(s):  
Liujun Lin
Keyword(s):  
Euclidean Distance ◽  
Clustering Algorithm ◽  
Color Space ◽  
Color Classification ◽  
Grade Prediction ◽  
Color Grading ◽  
Color Features ◽  
Automatic Clustering ◽  
Preliminary Study ◽  
Different Color

Abstract Traditionally, the color grading of sapphire is mainly based on the naked eye judgment of the appraiser. This judgment standard is not clear enough, and the judgment result has a greater subjective influence, which affects the accuracy of the classification. In this study, the GEM-3000 ultraviolet-visible spectrophotometer was selected, and the color features of 180 sapphire samples were extracted and classified using the CIE1976 color space of the device. The Kmeans algorithm was used to cluster analysis of 140 samples, and the separability of the color space features of different color levels was verified, and the center sample of each color level was obtained. The Euclidean distance between the centers of the remaining 40 samples is calculated, and each color grade prediction label is determined, and the sapphire color is automatically classified based on this. The experimental results show that the accuracy of sapphire color classification using the above method is 97.5%, which confirms the effect and accuracy of the artificial intelligence method in sapphire color classification.

scholarly journals Klasifikasi Untuk Menentukan Tingkat Kematangan Buah Pisang Sunpride

2016 ◽  
Vol 2 ◽  
pp. 109
Author(s):  
Gracelia Adelaida Bere ◽  
Elizabeth Nurmiyati Tamtjita ◽  
Anggraini Kusumaningrum
Keyword(s):  
Euclidean Distance ◽  
Color Space ◽  
Extraction Process ◽  
Classification Performance ◽  
Test Image ◽  
K Nearest Neighbors ◽  
Color Features ◽  
Training Samples ◽  
Rgb Color Space ◽  
Test Objects

         YIQ (Iuma, In-phase, Quadrature) is a color space used to transmit analog TV signal. This research is conducting a possibility test on using YIQ as color features for fruit ripeness classification, which tested on Sunpride bananas. Classification is done using k-NN algorithm against YIQ values of several ripeness stage. The classification process itself consists of two steps: training and testing. In the training step, values from RGB color space of the images as training samples are converted into YIQ and extracted as features to form the classes, while in testing step, the test image went through the same conversion and feature extraction process, then classified using k-NN against the classes’ features, using k=3 and k=1. There are 120 Sunpride banana images used as test objects, and the results obtained shown that the classification performance using k=3 for Sangat Matang class is 100%, Busuk class is 66,67%, Mengkal class is 60% and Matang class is 60%. Results using k=1 for Sangat Matang class is 100%, Busuk class is 66,67%, Mengkal class is 66,7% and Matang class is 56,67%.Keywords  : Classification, YIQ Color Space, Banana Sunpride, Euclidean Distance, k-Nearest Neighbors

scholarly journals Image Retrieval with Fusion of Thepade’s Sorted Block Truncation Coding n-ary based Color and Local Binary Pattern based Texture Features with Different Color Places

2020 ◽  
Vol 9 (5) ◽  
pp. 28-34
Keyword(s):  
Image Retrieval ◽  
Local Binary Pattern ◽  
Feature Vector ◽  
Color Space ◽  
Texture Features ◽  
Local Binary Patterns ◽  
Block Truncation Coding ◽  
Local Texture ◽  
Color Features ◽  
Different Color

In these years, there has been a gigantic growth in the generation of data. Innovations such as the Internet, social media and smart phones are the facilitators of this information boom. Since ancient times images were treated as an effective mode of communication. Even today most of the data generated is image data. The technology for capturing, storing and transferring images is well developed but efficient image retrieval is still a primitive area of research. Content Based Image Retrieval (CBIR) is one such area where lot of research is still going on. CBIR systems rely on three aspects of the image content namely texture, shape and color. Application specific CBIR systems are effective whereas Generic CBIR systems are being explored. Previously, descriptors are used to extract shape, color or texture content features, but the effect of using more than one descriptor is under research and may yield better results. The paper presents the fusion of TSBTC n-ary (Thepade's Sorted n-ary Block Truncation Coding) Global Color Features and Local Binary Pattern (LBP) Local Texture Features in Content Based Image with Different Color Places TSBTC n-ary devises global color features from an image. It is a faster and better technique compared to Block Truncation Coding. It is also rotation and scale invariant. When applied on an image TSBTC n-ary gives a feature vector based on the color space, if TSBTC n-ary is applied on the obtained LBP (Local Binary Patterns) of the image color planes, the feature vector obtained is be based on local texture content. Along with RGB, the Luminance chromaticity color space like YCbCr and Kekre’s LUV are also used in experimentation of proposed CBIR techniques. Wang dataset has been used for exploration of proposed method. It consists of 1000 images (10 categories having 100 images each). Obtained results have shown performance improvement using fusion of BTC extracted global color features and local texture features extracted with TSBTC n-ary applied on Local Binary Patterns (LBP).

scholarly journals Automatic Color Extraction Algorithm of Graphic Design Image Based on Artificial Intelligence

2022 ◽  
Vol 16 ◽  
pp. 374-384
Author(s):  
Qian Zhao ◽  
Hong Zhang
Keyword(s):  
Artificial Intelligence ◽  
Data Clustering ◽  
Clustering Algorithm ◽  
Color Space ◽  
Automatic Extraction ◽  
Basic Operation ◽  
Retrieval Process ◽  
The Past ◽  
Color Features ◽  
Extraction Algorithm

The extraction of color features plays an important role in image recognition and image retrieval. In the past, feature extraction mainly depends on manual or supervised learning, which limits the automation of the whole recognition or retrieval process. In order to solve the above problems, an automatic color extraction algorithm based on artificial intelligence is proposed. According to the characteristics of BMP image, the paper makes use of the conversion between image color space and realizes it in the visual C++6.0 environment. The experimental results show that the algorithm realizes the basic operation of image preprocessing, and realizes the automatic extraction of image color features by proper data clustering algorithm.

Segmentation of SAR Image using Fuzzy C-Means and Filters

2020 ◽  
Vol 8 (1) ◽  
pp. 84-90
Author(s):  
R. Lalchhanhima ◽  
◽  
Debdatta Kandar ◽  
R. Chawngsangpuii ◽  
Vanlalmuansangi Khenglawt ◽  
...  
Keyword(s):  
Clustering Algorithm ◽  
State Of The Art ◽  
Speckle Noise ◽  
Synthetic Aperture Radar Image ◽  
Synthetic Aperture ◽  
Sar Image ◽  
Spatial Filters ◽  
Fuzzy C Means ◽  
Automatic Clustering ◽  
Intensity Information

Fuzzy C-Means is an unsupervised clustering algorithm for the automatic clustering of data. Synthetic Aperture Radar Image Segmentation has been a challenging task because of the presence of speckle noise. Therefore the segmentation process can not directly rely on the intensity information alone but must consider several derived features in order to get satisfactory segmentation results. In this paper, it is attempted to use the fuzzy nature of classification for the purpose of unsupervised region segmentation in which FCM is employed. Different features are obtained by filtering of the image by using different spatial filters and are selected for segmentation criteria. The segmentation performance is determined by the accuracy compared with a different state of the art techniques proposed recently.

scholarly journals Automatic measurement of traditional Chinese costume from its silhouette through Fuzzy c-means clustering method

2020 ◽  
Vol 15 ◽  
pp. 155892502097832
Author(s):  
Jiaqin Zhang ◽  
Jingan Wang ◽  
Le Xing ◽  
Hui’e Liang
Keyword(s):  
Industrial Application ◽  
Clustering Algorithm ◽  
Color Space ◽  
Automatic Measurement ◽  
Feature Point ◽  
Feature Points ◽  
Point Location ◽  
Fuzzy C Means ◽  
Fuzzy C Means Clustering ◽  
Environmental Robustness

As the precious cultural heritage of the Chinese nation, traditional costumes are in urgent need of scientific research and protection. In particular, there are scanty studies on costume silhouettes, due to the reasons of the need for cultural relic protection, and the strong subjectivity of manual measurement, which limit the accuracy of quantitative research. This paper presents an automatic measurement method for traditional Chinese costume dimensions based on fuzzy C-means clustering and silhouette feature point location. The method is consisted of six steps: (1) costume image acquisition; (2) costume image preprocessing; (3) color space transformation; (4) object clustering segmentation; (5) costume silhouette feature point location; and (6) costume measurement. First, the relative total variation model was used to obtain the environmental robustness and costume color adaptability. Second, the FCM clustering algorithm was used to implement image segmentation to extract the outer silhouette of the costume. Finally, automatic measurement of costume silhouette was achieved by locating its feature points. The experimental results demonstrated that the proposed method could effectively segment the outer silhouette of a costume image and locate the feature points of the silhouette. The measurement accuracy could meet the requirements of industrial application, thus providing the dual value of costume culture research and industrial application.

A population-based automatic clustering algorithm for image segmentation

2021 ◽  
Author(s):  
Seyed Jalaleddin Mousavirad ◽  
Gerald Schaefer ◽  
Mahshid Helali Moghadam ◽  
Mehrdad Saadatmand ◽  
Mahdi Pedram
Keyword(s):  
Image Segmentation ◽  
Clustering Algorithm ◽  
Population Based ◽  
Automatic Clustering

scholarly journals Research on the Method of Coke Optical Tissue Segmentation Based on Adaptive Clustering

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Huaiguang Liu ◽  
Liheng Zhang ◽  
Shiyang Zhou ◽  
Li Fang
Keyword(s):  
Mahalanobis Distance ◽  
Euclidean Distance ◽  
Clustering Algorithm ◽  
Adaptive Threshold ◽  
Segmentation Method ◽  
Tissue Segmentation ◽  
Adaptive Clustering ◽  
Morphological Filtering ◽  
Key Factor ◽  
The Relationship

The microstructure is the key factor for quality discriminate of coke. In view of the characteristics of coke optical tissue (COT), a segmentation method of coke microstructures based on adaptive clustering was proposed. According to the strategy of multiresolution, adaptive threshold binarization and morphological filtering were carried out on COT images with lower resolution. The contour of the COT body was detected through the relationship checking between contours in the binary image, and hence, COT pixels were picked out to cluster for tissue segmentation. In order to get the optimum segmentation for each tissue, an advanced K -means method with adaptive clustering centers was provided according to the Calinski-Harabasz score. Meanwhile, Euclidean distance was substituted with Mahalanobis distance between each pixel in HSV space to improve the accuracy. The experimental results show that compared with the traditional K -means algorithm, FCM algorithm, and Meanshift algorithm, the adaptive clustering algorithm proposed in this paper is more accurate in the segmentation of various tissue components in COT images, and the accuracy of tissue segmentation reaches 94.3500%.

scholarly journals Energy efficient clustering using the AMHC (adoptive multi-hop clustering) technique

2020 ◽  
Vol 10 (2) ◽  
pp. 1622
Author(s):  
Vimala M. ◽  
Rajeev Ranjan
Keyword(s):  
Energy Efficient ◽  
Clustering Algorithm ◽  
Data Gathering ◽  
Principal Component ◽  
Independent Sets ◽  
Primary Component ◽  
Industry Applications ◽  
Lifetime Number ◽  
Different Color ◽  
Energy Efficient Clustering

IoT has gained fine attention in several field such as in industry applications, agriculture, monitoring, surveillance, similarly parallel growth has been observed in field of WSN. WSN is one of the primary component of IoT when it comes to sensing the data in various environment. Clustering is one of the basic approach in order to obtain the measurable performance in WSNs, Several algorithms of clustering aims to obtain the efficient data collection, data gathering and the routing. In this paper, a novel AMHC (Adaptive Multi-Hop Clustering) algorithm is proposed for the homogenous model, the main aim of algorithm is to obtain the higher efficiency and make it energy efficient. Our algorithm mainly contains the three stages: namely assembling, coupling and discarding. First stage involves the assembling of independent sets (maximum), second stage involves the coupling of independent sets and at last stage the superfluous nodes are discarded. Discarding superfluous nodes helps in achieving higher efficiency. Since our algorithm is a coloring algorithm, different color are used at the different stages for coloring the nodes. Afterwards our algorithm (AMHC) is compared with the existing system which is a combination of Second order data CC(Coupled Clustering) and Compressive-Projection PCA(Principal Component Analysis), and results shows that our algorithm excels in terms of several parameters such as energy efficiency, network lifetime, number of rounds performed.

scholarly journals What we talk about when we talk about color

2020 ◽  
Author(s):  
Colin R. Twomey ◽  
Gareth Roberts ◽  
David Brainard ◽  
Joshua B. Plotkin
Keyword(s):  
Visible Light ◽  
Color Space ◽  
Color Perception ◽  
Geographic Location ◽  
Cross Cultural ◽  
Cultural Variation ◽  
Inference Algorithm ◽  
Color Categories ◽  
New Directions ◽  
Different Color

Names for colors vary widely across languages, but color categories are remarkably consistent [1–5]. Shared mechanisms of color perception help explain consistent partitions of visible light into discrete color vocabularies [6–10]. But the mappings from colors to words are not identical across languages, which may reflect communicative needs – how often speakers must refer to objects of different color [11]. Here we quantify the communicative needs of colors in 130 different languages, using a novel inference algorithm. Some regions of color space exhibit 30-fold greater demand for communication than other regions. The regions of greatest demand correlate with the colors of salient objects, including ripe fruits in primate diets. Using the mathematics of compression we predict and empirically test how languages map colors to words, accounting for communicative needs. We also document extensive cultural variation in communicative demands on different regions of color space, which is partly explained by differences in geographic location and local biogeography. This account reconciles opposing theories for universal patterns in color vocabularies, while opening new directions to study cross-cultural variation in the need to communicate different colors.

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