Xiaoning Song

67 papers receiving 1.2k citations

Peers

Xiaoning Song
Comparison fields: 5 of 81
  • Computational Theory and Mathematics 615
  • Computer Vision and Pattern Recognition 370
  • Information Systems 329
  • Artificial Intelligence 456
  • Signal Processing 145
Replace Can Gao with:
Can Gao China
Yanyong Huang China
H. Hannah Inbarani India
Jarosław Stepaniuk Poland
Daniel Paternain Spain
Wei-Zhi Wu China
Tingquan Deng China
Jinjin Li China
Hengrong Ju China
Hegui Zhu China
Xiaoning Song relative to Can Gao China Can Gao's profile →
Citations per field
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Citations per year

Countries citing papers authored by Xiaoning Song

Since Specialization
Citations

This map shows the geographic impact of Xiaoning Song'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 Xiaoning Song with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Xiaoning Song more than expected).

Fields of papers citing papers by Xiaoning Song

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Xiaoning Song. 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 Xiaoning Song. The network helps show where Xiaoning Song may publish in the future.

Co-authors

The 25 scholars most cited alongside Xiaoning Song, 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 Xiaoning Song Line = papers co-authored together Xiaoning Song links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 68 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2013120
2 2014102
3 202189
4 201169
5 201363
6 201959
7 201756
8 201547
9 201440
10 200940
11 201732
12 202229
13 201727
14 201826
15 201623
16 200823
17 202123
18 201821
19 201520
20 202019

About Xiaoning Song

Xiaoning Song is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence, Computational Theory and Mathematics, Computational Mechanics and Signal Processing, having authored 68 papers that have together received 1.2k indexed citations. Recurring topics across this work include Face and Expression Recognition (30 papers), Face recognition and analysis (17 papers), Sparse and Compressive Sensing Techniques (14 papers), Rough Sets and Fuzzy Logic (14 papers), Data Mining Algorithms and Applications (8 papers), Biometric Identification and Security (7 papers), Remote-Sensing Image Classification (7 papers) and Image Retrieval and Classification Techniques (5 papers). The work is most often cited by research in Computational Theory and Mathematics (615 citations), Computer Vision and Pattern Recognition (370 citations), Information Systems (329 citations), Artificial Intelligence (456 citations) and Signal Processing (145 citations). Xiaoning Song has collaborated with scholars based in China, United Kingdom and United States. Frequent co-authors include Xibei Yang, Jingyu Yang, Zhenhua Feng, Yunsong Qi, Xiao‐Jun Wu, Dong‐Jun Yu, Hualong Yu, Shuang Wu, Guosheng Hu and Yong Qi. Their work appears in journals such as International Journal of Machine Learning and Cybernetics, Soft Computing, Neurocomputing, Knowledge-Based Systems and The Journal of Engineering.

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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