Ondřej Chum

131 total papers · 18.6k total citations
62 papers, 10.6k citations indexed

About

Ondřej Chum is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Aerospace Engineering. According to data from OpenAlex, Ondřej Chum has authored 62 papers receiving a total of 10.6k indexed citations (citations by other indexed papers that have themselves been cited), including 54 papers in Computer Vision and Pattern Recognition, 18 papers in Artificial Intelligence and 16 papers in Aerospace Engineering. Recurrent topics in Ondřej Chum's work include Advanced Image and Video Retrieval Techniques (47 papers), Image Retrieval and Classification Techniques (21 papers) and Robotics and Sensor-Based Localization (16 papers). Ondřej Chum is often cited by papers focused on Advanced Image and Video Retrieval Techniques (47 papers), Image Retrieval and Classification Techniques (21 papers) and Robotics and Sensor-Based Localization (16 papers). Ondřej Chum collaborates with scholars based in Czechia, United Kingdom and United States. Ondřej Chum's co-authors include Jiřı́ Matas, Tomáš Pajdla, M. Urban, James Philbin, Andrew Zisserman, Michael Isard, Josef Šivic, Michal Perďoch, Andrew Zisserman and Jan‐Michael Frahm and has published in prestigious journals such as IEEE Transactions on Pattern Analysis and Machine Intelligence, International Journal of Computer Vision and Image and Vision Computing.

In The Last Decade

Ondřej Chum

57 papers receiving 10.1k citations

Hit Papers

Robust wide-baseline ster... 2002 2026 2010 2018 2004 2007 2008 2002 2005 500 1000 1.5k 2.0k
What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation benchmark for papers published in the same subfield and year, or reaches the top citation threshold in at least one of its specific research topics.

  1. Robust wide-baseline stereo from maximally stable extremal regions
    2004 2.5k citations Jiřı́ Matas, Ondřej Chum et al. Image and Vision Computing profile →
  2. Object retrieval with large vocabularies and fast spatial matching
    2007 1.9k citations James Philbin, Ondřej Chum et al. profile →
  3. Lost in quantization: Improving particular object retrieval in large scale image databases
    2008 942 citations James Philbin, Ondřej Chum et al. profile →
  4. Robust Wide Baseline Stereo from Maximally Stable Extremal Regions
    2002 826 citations Jiřı́ Matas, Ondřej Chum et al. profile →
  5. Matching with PROSAC — Progressive Sample Consensus
    2005 716 citations Ondřej Chum, Jiřı́ Matas profile →
  6. Total Recall: Automatic Query Expansion with a Generative Feature Model for Object Retrieval
    2007 579 citations Ondřej Chum, James Philbin et al. profile →
  7. USAC: A Universal Framework for Random Sample Consensus
    2013 444 citations Rahul Raguram, Ondřej Chum et al. IEEE Transactions on Pattern Analysis and Machine Intelligence profile →
  8. Near Duplicate Image Detection: min-Hash and tf-idf Weighting
    2008 322 citations Ondřej Chum, James Philbin et al. Zenodo (CERN European Organization for Nuclear Research) profile →

Author Peers

Peers are selected by citation overlap in the author's most active subfields. citations · hero ref

Author Last Decade Papers Cites
Ondřej Chum 9.6k 4.1k 1.3k 1.3k 478 62 10.6k
Josef Šivic 12.2k 1.3× 3.4k 0.8× 1.5k 1.2× 3.0k 2.3× 449 0.9× 98 14.3k
Chris Harris 7.3k 0.8× 4.0k 1.0× 1.0k 0.8× 486 0.4× 571 1.2× 25 8.9k
Matthew J. Stephens 7.1k 0.7× 3.9k 0.9× 989 0.8× 544 0.4× 555 1.2× 20 8.9k
Tomáš Pajdla 8.5k 0.9× 4.9k 1.2× 1.3k 1.0× 694 0.5× 943 2.0× 161 10.1k
James Hays 8.1k 0.8× 1.3k 0.3× 913 0.7× 2.2k 1.7× 337 0.7× 80 9.9k
Philip H. S. Torr 6.2k 0.6× 2.1k 0.5× 568 0.4× 1.1k 0.8× 786 1.6× 121 7.7k
Bastian Leibe 10.7k 1.1× 3.2k 0.8× 1.2k 0.9× 2.1k 1.6× 671 1.4× 159 12.5k
Tony Lindeberg 6.8k 0.7× 1.6k 0.4× 1.4k 1.0× 1.0k 0.8× 163 0.3× 89 9.2k
Derek Hoiem 7.4k 0.8× 1.3k 0.3× 871 0.7× 3.3k 2.6× 549 1.1× 80 9.2k
D. Nistér 8.7k 0.9× 5.8k 1.4× 940 0.7× 499 0.4× 1.2k 2.6× 55 10.0k

Countries citing papers authored by Ondřej Chum

Since Specialization
Citations

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

Fields of papers citing papers by Ondřej Chum

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Ondřej Chum

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

All Works

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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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