Cor J. Veenman

39 papers receiving 1.6k citations

Hit Papers

Visual Word Ambiguity20092026201420202009100200300400500

Peers

Cor J. Veenman
Comparison fields: 5 of 126
  • Computer Vision and Pattern Recognition 1.1k
  • Artificial Intelligence 635
  • Media Technology 202
  • Signal Processing 169
  • Molecular Biology 132
Replace Kap Luk Chan with:
Kap Luk Chan Singapore
Jyri Kivinen United Kingdom
Xinzhong Zhu China
Martin Heusel Austria
Xianglong Tang China
Xin Jin China
Francesco Camastra Italy
E. Backer Netherlands
Martin Szummer United Kingdom
宏治 津田 Japan
Cor J. Veenman relative to Kap Luk Chan Singapore Kap Luk Chan's profile →
Citations per field
00.5×3.3×
Kap Luk Chan · 1×
Citations per year

Countries citing papers authored by Cor J. Veenman

Since Specialization
Citations

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

Fields of papers citing papers by Cor J. Veenman

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Cor J. Veenman. 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 Cor J. Veenman. The network helps show where Cor J. Veenman may publish in the future.

Co-authorship network of co-authors of Cor J. Veenman

This figure shows the co-authorship network connecting the top 25 collaborators of Cor J. Veenman. A scholar is included among the top collaborators of Cor J. Veenman 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 Cor J. Veenman. Cor J. Veenman 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
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Authorship Verification with Compression Features.
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Bootstrapped Authorship Attribution in Compression Space.
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The MediaMill TRECVID 2005 Semantic Video Search Engine (Draft Version).
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Tuning the hyperparameter of an AUC-optimized classifier
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About Cor J. Veenman

Cor J. Veenman is a scholar working on Computer Vision and Pattern Recognition, Health Informatics and Artificial Intelligence, having authored 45 papers that have together received 1.7k indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (6 papers), Digital and Cyber Forensics (5 papers) and Imbalanced Data Classification Techniques (4 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (1.1k citations), Media Technology (202 citations) and Artificial Intelligence (635 citations). Cor J. Veenman has collaborated with scholars based in Netherlands, United States and France. Frequent co-authors include Marcel Reinders, E. Backer, Jan van Gemert, Jan‐Mark Geusebroek, A.W.M. Smeulders, Cees G. M. Snoek, David M. J. Tax, Laura van ‘t Veer, Lodewyk F.A. Wessels and Yudong D. He. Their work appears in journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, Remote Sensing of Environment and IEEE Transactions on Image Processing.

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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