Ming Cheung

572 total citations
33 papers, 393 citations indexed

About

Ming Cheung is a scholar working on Computer Vision and Pattern Recognition, Information Systems and Artificial Intelligence. According to data from OpenAlex, Ming Cheung has authored 33 papers receiving a total of 393 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Computer Vision and Pattern Recognition, 10 papers in Information Systems and 8 papers in Artificial Intelligence. Recurrent topics in Ming Cheung's work include Advanced Image and Video Retrieval Techniques (7 papers), Complex Network Analysis Techniques (6 papers) and Image Retrieval and Classification Techniques (6 papers). Ming Cheung is often cited by papers focused on Advanced Image and Video Retrieval Techniques (7 papers), Complex Network Analysis Techniques (6 papers) and Image Retrieval and Classification Techniques (6 papers). Ming Cheung collaborates with scholars based in Hong Kong, China and Macao. Ming Cheung's co-authors include James She, Jiantao Zhou, Weiwei Sun, Yuanman Li, Zhanming Jie, Yihua Cui, Jie Tao, Xiaopeng Li, Soochang Park and Alvin Junus and has published in prestigious journals such as IEEE Transactions on Circuits and Systems for Video Technology, IEEE Transactions on Multimedia and Journal of Reinforced Plastics and Composites.

In The Last Decade

Ming Cheung

32 papers receiving 383 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ming Cheung Hong Kong 10 187 77 70 44 43 33 393
Rochdi Messoussi Morocco 13 216 1.2× 140 1.8× 58 0.8× 29 0.7× 7 0.2× 43 487
Funda Durupınar United States 9 131 0.7× 86 1.1× 11 0.2× 54 1.2× 5 0.1× 24 454
Arun Kumar Yadav India 11 83 0.4× 165 2.1× 67 1.0× 21 0.5× 14 0.3× 61 361
Lingyun Yu China 13 330 1.8× 81 1.1× 10 0.1× 56 1.3× 30 471
Kai-Hsiang Yang Taiwan 13 27 0.1× 96 1.2× 133 1.9× 29 0.7× 12 0.3× 54 464
Deokgun Park United States 8 238 1.3× 163 2.1× 52 0.7× 59 1.3× 10 407
Zachary Pousman United States 9 309 1.7× 60 0.8× 64 0.9× 106 2.4× 10 671
Siyuan Zhao China 9 33 0.2× 231 3.0× 60 0.9× 12 0.3× 88 2.0× 22 418
Sriram Karthik Badam United States 13 383 2.0× 98 1.3× 32 0.5× 75 1.7× 25 559
Vladimir Geroimenko United Kingdom 10 122 0.7× 95 1.2× 70 1.0× 35 0.8× 25 300

Countries citing papers authored by Ming Cheung

Since Specialization
Citations

This map shows the geographic impact of Ming Cheung's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Ming Cheung with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ming Cheung more than expected).

Fields of papers citing papers by Ming Cheung

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Ming Cheung. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Ming Cheung. The network helps show where Ming Cheung may publish in the future.

Co-authorship network of co-authors of Ming Cheung

This figure shows the co-authorship network connecting the top 25 collaborators of Ming Cheung. A scholar is included among the top collaborators of Ming Cheung based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ming Cheung. Ming Cheung is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
2.
Sun, Weiwei, Jiantao Zhou, Yuanman Li, Ming Cheung, & James She. (2020). Robust High-Capacity Watermarking Over Online Social Network Shared Images. IEEE Transactions on Circuits and Systems for Video Technology. 31(3). 1208–1221. 73 indexed citations
3.
Cheung, Ming & James She. (2019). Detecting Social Signals in User-Shared Images for Connection Discovery Using Deep Learning. IEEE Transactions on Multimedia. 22(2). 407–420. 6 indexed citations
4.
She, James, et al.. (2019). Visual Arts Search on Mobile Devices. ACM Transactions on Multimedia Computing Communications and Applications. 15(2s). 1–23. 6 indexed citations
5.
Cheung, Ming, James She, Weiwei Sun, & Jiantao Zhou. (2019). Detecting Online Counterfeit-goods Seller using Connection Discovery. ACM Transactions on Multimedia Computing Communications and Applications. 15(2). 1–16. 5 indexed citations
6.
Cheung, Ming, Xiaopeng Li, & James She. (2017). An Efficient Computation Framework for Connection Discovery using Shared Images. ACM Transactions on Multimedia Computing Communications and Applications. 13(4). 1–21. 6 indexed citations
7.
Cheung, Ming, et al.. (2017). Effectiveness of Mobile Notification Delivery. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 21–29. 11 indexed citations
8.
Cheung, Ming & James She. (2017). An Analytic System for User Gender Identification through User Shared Images. ACM Transactions on Multimedia Computing Communications and Applications. 13(3). 1–20. 4 indexed citations
9.
Cheung, Ming, James She, & Ning Wang. (2017). Characterizing User Connections in Social Media through User-Shared Images. IEEE Transactions on Big Data. 4(4). 447–458. 11 indexed citations
10.
Cheung, Ming, et al.. (2017). DeepArt. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 1183–1191. 56 indexed citations
11.
Cheung, Ming, et al.. (2016). Prediction of Virality Timing Using Cascades in Social Media. ACM Transactions on Multimedia Computing Communications and Applications. 13(1). 1–23. 9 indexed citations
12.
Li, Xiaopeng, Ming Cheung, & James She. (2016). Connection discovery using shared images by Gaussian relational topic model. arXiv (Cornell University). 931–936. 8 indexed citations
13.
Cheung, Ming, James She, & Soochang Park. (2016). Analytics-Driven Visualization on Digital Directory via Screen-Smart Device Interactions. IEEE Transactions on Multimedia. 18(11). 2303–2314. 2 indexed citations
14.
Cheung, Ming, James She, & Zhanming Jie. (2015). Connection Discovery Using Big Data of User-Shared Images in Social Media. IEEE Transactions on Multimedia. 17(9). 1417–1428. 32 indexed citations
15.
Junus, Alvin, Ming Cheung, James She, & Zhanming Jie. (2015). Community-Aware Prediction of Virality Timing Using Big Data of Social Cascades. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 487–492. 5 indexed citations
16.
Cheung, Ming & James She. (2014). Bag-of-Features Tagging Approach for a Better Recommendation with Social Big Data. 83–88. 11 indexed citations
17.
Cheung, Ming, et al.. (2014). Analyzing the User Inactiveness in a Mobile Social Game. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 415–420. 4 indexed citations
18.
She, James, et al.. (2013). Cyber-physical Directory: A Dynamic Visualization of Social Media Data. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 2007–2012. 2 indexed citations
19.
Cheung, Ming. (2011). Factors Affecting the Design of Electronic Direct Mail Messages: Implications for Professional Communicators. IEEE Transactions on Professional Communication. 54(3). 279–298. 4 indexed citations
20.
Teillet, Philippe, G. Fedosejevs, Robert P. Gauthier, et al.. (2007). An integrated Earth sensing sensorweb for improved crop and rangeland yield predictions. Canadian Journal of Remote Sensing. 33(2). 88–98. 12 indexed citations

Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.

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