Ming Cheung

571 citations
32 papers · 396 · h-index 10

Impact in

Papers in

Ming Cheung

31 papers receiving 387 citations

Peers

Ming Cheung
Comparison fields: 5 of 80
  • Computer Vision and Pattern Recognition 193
  • Computer Graphics and Computer-Aided Design 11
  • Polymers and Plastics 44
  • Information Systems 69
  • Conservation 10
Replace Yonghui Dai with:
Yonghui Dai China
Rochdi Messoussi Morocco
Jingan Wang China
Inmaculada Rodríguez Spain
Jiaxin Wu China
Eamonn O’Brien-Strain United States
Yi-Hui Chen Taiwan
Muhammad Faisal Cheema Pakistan
Nan Yu China
Zhenyu Cheryl Qian United States
Ming Cheung relative to Yonghui Dai China Yonghui Dai's profile →
Citations per field
00.5×11×
Yonghui Dai · 1×
Citations per year

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-authors

The 22 scholars most cited alongside Ming Cheung, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Ming Cheung Line = papers co-authored together Ming Cheung links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 32 papers — load more, or switch the sort, to bring in the rest.

#Work
1 202077
2 201758
3 200851
4 201532
5 200826
6 201712
7 200712
8
Bag-of-Features Tagging Approach for a Better Recommendation with Social Big Data
201411
9 201711
10 20169
11 20188
12 20158
13 20168
14 20196
15 20176
16 20196
17 20166
18 20166
19 20195
20 20165

About Ming Cheung

Ming Cheung is a scholar working on Computer Vision and Pattern Recognition, Information Systems, Artificial Intelligence, Sociology and Political Science and Statistical and Nonlinear Physics, having authored 32 papers that have together received 396 indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (7 papers), Image Retrieval and Classification Techniques (6 papers), Complex Network Analysis Techniques (5 papers), Spam and Phishing Detection (4 papers), Caching and Content Delivery (4 papers), Digital Marketing and Social Media (3 papers), Human Mobility and Location-Based Analysis (3 papers) and Web Data Mining and Analysis (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (193 citations), Computer Graphics and Computer-Aided Design (11 citations), Polymers and Plastics (44 citations), Information Systems (69 citations) and Conservation (10 citations). Ming Cheung has collaborated with scholars based in Hong Kong, China and Macao. Frequent co-authors include James She, Jiantao Zhou, Yuanman Li, Weiwei Sun, Zhanming Jie, Yihua Cui, Jie Tao, Xiaopeng Li, Ning Wang and Soochang Park. Their work appears in journals such as ACM Transactions on Multimedia Computing Communications and Applications, IEEE Transactions on Multimedia, Semiotica, IEEE Transactions on Big Data and IEEE Transactions on Circuits and Systems for Video Technology.

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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