Chengmao Wu

600 total citations
60 papers, 416 citations indexed

About

Chengmao Wu is a scholar working on Computer Vision and Pattern Recognition, Media Technology and Artificial Intelligence. According to data from OpenAlex, Chengmao Wu has authored 60 papers receiving a total of 416 indexed citations (citations by other indexed papers that have themselves been cited), including 47 papers in Computer Vision and Pattern Recognition, 32 papers in Media Technology and 19 papers in Artificial Intelligence. Recurrent topics in Chengmao Wu's work include Remote-Sensing Image Classification (30 papers), Face and Expression Recognition (20 papers) and Advanced Clustering Algorithms Research (17 papers). Chengmao Wu is often cited by papers focused on Remote-Sensing Image Classification (30 papers), Face and Expression Recognition (20 papers) and Advanced Clustering Algorithms Research (17 papers). Chengmao Wu collaborates with scholars based in China and Australia. Chengmao Wu's co-authors include Han Liu, Yan Chen, Xue Zhang, Zhiqin Kang, Jiajia Zhang, Xiaoping Tian, Xue Zhang, Xiaoyi Feng, D.C. Yu and Congcong Huang and has published in prestigious journals such as Expert Systems with Applications, IEEE Access and Pattern Recognition.

In The Last Decade

Chengmao Wu

48 papers receiving 400 citations

Peers

Chengmao Wu

CVPR

CTM

SNP

Jidong Zhao China

Xuesong Yin China

Taisong Jin China

Dongxia Chang China

Tingjin Luo China

Yingkang Hu United States

Khalid Zenkouar Morocco

Wang-Tao Zhou China

Ashish Girdhar India

Jidong Zhao China

Chengmao Wu

312 ×1.0

CVPR

243 ×1.7

49 ×0.4

13 ×0.3

CTM

7 ×0.3

SNP

Citations per year, relative to Chengmao Wu Chengmao Wu (= 1×) peers Jidong Zhao

Countries citing papers authored by Chengmao Wu

Since Specialization

Citations

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

Fields of papers citing papers by Chengmao Wu

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Chengmao Wu

This figure shows the co-authorship network connecting the top 25 collaborators of Chengmao Wu. A scholar is included among the top collaborators of Chengmao Wu 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 Chengmao Wu. Chengmao Wu is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Wu, Chengmao, et al.. (2025). A novel multi-means joint learning framework based on fuzzy clustering and self-constrained spectral clustering for superpixel image segmentation. Computers & Electrical Engineering. 124. 110358–110358. 2 indexed citations

Wu, Chengmao, et al.. (2025). A novel joint learning framework combining fuzzy C-multiple-means clustering and spectral clustering for superpixel-based image segmentation. Digital Signal Processing. 161. 105083–105083. 1 indexed citations

Wu, Chengmao & Shing‐Tai Pan. (2025). Fuzzy C-Poincaré Fréchet means clustering in hyperbolic space. Expert Systems with Applications. 288. 128245–128245.

Wu, Chengmao, et al.. (2025). An improved memristive model driven cellular neural networks for highly efficient advanced image processing. Physica Scripta. 100(3). 35956–35956.

Wu, Chengmao, et al.. (2025). Generalized harmonic fuzzy partition C-means clustering. The Journal of Supercomputing. 81(2).

Wu, Chengmao, et al.. (2024). Generalized multiplicative fuzzy possibilistic product partition C-means clustering. Information Sciences. 670. 120588–120588. 4 indexed citations

Wu, Chengmao & Xiaogang Qi. (2024). Reconstruction-Aware Kernelized Fuzzy Clustering Framework Incorporating Local Information for Image Segmentation. Neural Processing Letters. 56(2). 1 indexed citations

Wu, Chengmao, et al.. (2024). Joint learning framework of superpixel generation and fuzzy sparse subspace clustering for color image segmentation. Signal Processing. 222. 109515–109515. 7 indexed citations

Wu, Chengmao, et al.. (2024). Interval type-2 possibilistic picture C-means clustering incorporating local information for noisy image segmentation. Digital Signal Processing. 149. 104492–104492. 1 indexed citations

10.

Jiang, Yuheng, et al.. (2024). Fast spectral clustering with local cosine similarity graphs for hyperspectral images. Journal of Applied Remote Sensing. 18(2). 2 indexed citations

11.

Wu, Chengmao & Yulong Gao. (2024). Fast multiplicative fuzzy partition C-means clustering with a new membership scaling scheme. Engineering Applications of Artificial Intelligence. 142. 109854–109854.

12.

Wu, Chengmao, et al.. (2024). Rough Fuzzy Multi-view Clustering Based on Self-Weighting Strategy. 525–539.

13.

Wu, Chengmao, et al.. (2023). Total-aware suppressed possibilistic c-means clustering. Measurement. 219. 113183–113183. 3 indexed citations

14.

Wu, Chengmao & D.C. Yu. (2023). Generalized possibilistic c-means clustering with double weighting exponents. Information Sciences. 645. 119283–119283. 7 indexed citations

15.

Wu, Chengmao, et al.. (2023). Robust superpixel-based fuzzy possibilistic clustering method incorporating local information for image segmentation. The Visual Computer. 40(11). 7961–8000. 5 indexed citations

16.

Wu, Chengmao, et al.. (2023). Robust spectral clustering integrated nonlocal information blocks for hyperspectral image classification. Journal of Applied Remote Sensing. 17(1).

17.

Wu, Chengmao, et al.. (2023). Parameter-Free Auto-Weighted Possibilistic Fuzzy C-Means Clustering with Kernel Metric and Robust Algorithm. SN Computer Science. 4(4). 2 indexed citations

18.

Wu, Chengmao, et al.. (2020). Robust Semisupervised Kernelized Fuzzy Local Information C-Means Clustering for Image Segmentation. Mathematical Problems in Engineering. 2020. 1–22. 6 indexed citations

19.

Wu, Chengmao, et al.. (2019). Robust credibilistic fuzzy local information clustering with spatial information constraints. Digital Signal Processing. 97. 102615–102615. 19 indexed citations

20.

Fan, Jiulun, Chengmao Wu, & Yuanliang Ma. (2002). A modified partition coefficient. 3. 1496–1499. 3 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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