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.

An Empirical Comparison of Latest Data Clustering Algorithms with State-of-the-Art

2017 ◽  
Vol 5 (2) ◽  
pp. 410 ◽  
Author(s):  
Xianjin Shi ◽  
Wanwan Wang ◽  
Chongsheng Zhang
Keyword(s):  
Data Clustering ◽  
Spectral Clustering ◽  
Clustering Algorithm ◽  
State Of The Art ◽  
Clustering Algorithms ◽  
Density Peak ◽  
The Past ◽  
Overall Performance ◽  
Public Datasets ◽  
Clustering Validation

Over the past few decades, a great many data clustering algorithms have been developed, including K-Means, DBSCAN, Bi-Clustering and Spectral clustering, etc. In recent years, two new data  clustering algorithms have been proposed, which are affinity propagation (AP, 2007) and density peak based clustering (DP, 2014). In this work, we empirically compare the performance of these two latest data clustering algorithms with state-of-the-art, using 6 external and 2 internal clustering validation metrics. Our experimental results on 16 public datasets show that, the two latest clustering algorithms, AP and DP, do not always outperform DBSCAN. Therefore, to find the best clustering algorithm for a specific dataset, all of AP, DP and DBSCAN should be considered.  Moreover, we find that the comparison of different clustering algorithms is closely related to the clustering evaluation metrics adopted. For instance, when using the Silhouette clustering validation metric, the overall performance of K-Means is as good as AP and DP. This work has important reference values for researchers and engineers who need to select appropriate clustering algorithms for their specific applications.

scholarly journals Automatic Extraction of Color Features from Landscape Images Based on Image Processing

2021 ◽  
Vol 38 (3) ◽  
pp. 747-755
Author(s):  
Cong Tan ◽  
Shaoyu Yang
Keyword(s):  
Image Processing ◽  
Color Space ◽  
Color Constancy ◽  
Support Vector ◽  
Automatic Extraction ◽  
Color Features ◽  
Image Library ◽  
Presentation Effect ◽  
High Level

The dominant color features determine the presentation effect and visual experience of landscapes. The existing studies rarely quantify the application effect of landscape colors through image colorization. Besides, it is unreasonable to analyze landscape images with multiple standard colors with a single color space. To solve the problem, this paper proposes an automatic extraction method for color features from landscape images based on image processing. Firstly, a landscape lighting model was constructed based on color constancy theories, and the quality of landscape images was improved with color constant image enhancement technology. In this way, the low-level color features were extracted from the landscape image library. Next, support vector machine (SVM) and fuzzy c-means (FCM) were innovatively integrated to extract high-level color features from landscape images. The proposed method was proved effective through experiments.

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.

Balanced Data Clustering Algorithm for Both Hard and Soft Clustering

2018 ◽  
Vol 6 (2) ◽  
pp. 176-183
Author(s):  
Purnendu Das ◽  
◽  
Bishwa Ranjan Roy ◽  
Saptarshi Paul ◽  
◽  
...  
Keyword(s):  
Data Clustering ◽  
Clustering Algorithm ◽  
Soft Clustering

The Future of Work and the Changing Workplace

2018 ◽  
Author(s):  
Mahesh K. Joshi ◽  
J.R. Klein
Keyword(s):  
Artificial Intelligence ◽  
Machine Intelligence ◽  
Political Issue ◽  
Digital Innovation ◽  
Future Of Work ◽  
The Past ◽  
The World ◽  
Public Debates ◽  
The Future ◽  
Market Opportunities

The world of work has been impacted by technology. Work is different than it was in the past due to digital innovation. Labor market opportunities are becoming polarized between high-end and low-end skilled jobs. Migration and its effects on employment have become a sensitive political issue. From Buffalo to Beijing public debates are raging about the future of work. Developments like artificial intelligence and machine intelligence are contributing to productivity, efficiency, safety, and convenience but are also having an impact on jobs, skills, wages, and the nature of work. The “undiscovered country” of the workplace today is the combination of the changing landscape of work itself and the availability of ill-fitting tools, platforms, and knowledge to train for the requirements, skills, and structure of this new age.

scholarly journals A Study of Automatic Judgment of Food Color and Cooking Conditions with Artificial Intelligence Technology

Processes ◽  
2021 ◽  
Vol 9 (7) ◽  
pp. 1128
Author(s):  
Chern-Sheng Lin ◽  
Yu-Ching Pan ◽  
Yu-Xin Kuo ◽  
Ching-Kun Chen ◽  
Chuen-Lin Tien
Keyword(s):  
Artificial Intelligence ◽  
Image Processing ◽  
Color Space ◽  
Color Difference ◽  
Color Space Conversion ◽  
Before And After ◽  
Artificial Intelligence Technology ◽  
Artificial Intelligence Algorithm ◽  
Time Image ◽  
Cooking Conditions

In this study, the machine vision and artificial intelligence algorithms were used to rapidly check the degree of cooking of foods and avoid the over-cooking of foods. Using a smart induction cooker for heating, the image processing program automatically recognizes the color of the food before and after cooking. The new cooking parameters were used to identify the cooking conditions of the food when it is undercooked, cooked, and overcooked. In the research, the camera was used in combination with the software for development, and the real-time image processing technology was used to obtain the information of the color of the food, and through calculation parameters, the cooking status of the food was monitored. In the second year, using the color space conversion, a novel algorithm, and artificial intelligence, the foreground segmentation was used to separate the vegetables from the background, and the cooking ripeness, cooking unevenness, oil glossiness, and sauce absorption were calculated. The image color difference and the distribution were used to judge the cooking conditions of the food, so that the cooking system can identify whether or not to adopt partial tumbling, or to end a cooking operation. A novel artificial intelligence algorithm is used in the relative field, and the error rate can be reduced to 3%. This work will significantly help researchers working in the advanced cooking devices.

Artificial Intelligence and Human Rights: Four Realms of Discussion: Summary of Remarks

2020 ◽  
Vol 114 ◽  
pp. 242-245
Author(s):  
Jootaek Lee
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Making ◽  
Deep Learning ◽  
Language Processing ◽  
Human Reasoning ◽  
The Past ◽  
Making Choices ◽  
Data Capturing ◽  
Intensive Procedures

The term, Artificial Intelligence (AI), has changed since it was first coined by John MacCarthy in 1956. AI, believed to have been created with Kurt Gödel's unprovable computational statements in 1931, is now called deep learning or machine learning. AI is defined as a computer machine with the ability to make predictions about the future and solve complex tasks, using algorithms. The AI algorithms are enhanced and become effective with big data capturing the present and the past while still necessarily reflecting human biases into models and equations. AI is also capable of making choices like humans, mirroring human reasoning. AI can help robots to efficiently repeat the same labor intensive procedures in factories and can analyze historic and present data efficiently through deep learning, natural language processing, and anomaly detection. Thus, AI covers a spectrum of augmented intelligence relating to prediction, autonomous intelligence relating to decision making, automated intelligence for labor robots, and assisted intelligence for data analysis.

scholarly journals Boundaries Between Research Ethics and Ethical Research Use in Artificial Intelligence Health Research

2021 ◽  
pp. 155626462110027
Author(s):  
Gabrielle Samuel ◽  
Jenn Chubb ◽  
Gemma Derrick
Keyword(s):  
Artificial Intelligence ◽  
Health Research ◽  
Decision Makers ◽  
Societal Impact ◽  
The Past ◽  
Research And Innovation ◽  
The United Kingdom ◽  
Responsible Research ◽  
Past Half

The governance of ethically acceptable research in higher education institutions has been under scrutiny over the past half a century. Concomitantly, recently, decision makers have required researchers to acknowledge the societal impact of their research, as well as anticipate and respond to ethical dimensions of this societal impact through responsible research and innovation principles. Using artificial intelligence population health research in the United Kingdom and Canada as a case study, we combine a mapping study of journal publications with 18 interviews with researchers to explore how the ethical dimensions associated with this societal impact are incorporated into research agendas. Researchers separated the ethical responsibility of their research with its societal impact. We discuss the implications for both researchers and actors across the Ethics Ecosystem.

The Future of the Planetary Trust in a Kaleidoscopic World

2021 ◽  
pp. 1-8
Author(s):  
Edith Brown Weiss
Keyword(s):  
Artificial Intelligence ◽  
Natural Resource ◽  
Human Activities ◽  
New Technologies ◽  
Future Generations ◽  
Nuclear War ◽  
Human Environment ◽  
The Past ◽  
Resource Degradation ◽  
Shared Goal

Today, it is evident that we are part of a planetary trust. Conserving our planet represents a public good, global as well as local. The threats to future generations resulting from human activities make applying the normative framework of a planetary trust even more urgent than in the past decades. Initially, the planetary trust focused primarily on threats to the natural system of our human environment such as pollution and natural resource degradation, and on threats to cultural heritage. Now, we face a higher threat of nuclear war, cyber wars, and threats from gene drivers that can cause inheritable changes to genes, potential threats from other new technologies such as artificial intelligence, and possible pandemics. In this context, it is proposed that in the kaleidoscopic world, we must engage all the actors to cooperate with the shared goal of caring for and maintaining planet Earth in trust for present and future generations.

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.

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