Oksana Yakhnenko
- Computer Vision and Pattern Recognition top 10%
- Artificial Intelligence
- Signal Processing
- Molecular Biology
- Media Technology
- Co-authors
- Vasant HonavarAdrian SilvescuNicolas UsunierJakob VerbeekCordelia SchmidJason WestonAntoine BordesBalaji Krishnapuram
- Topics
- Image Retrieval and Classification Techniques (5 papers)Advanced Image and Video Retrieval Techniques (3 papers)Bayesian Methods and Mixture Models (2 papers)
- Journals
- ACM SIGKDD Explorations NewsletterHAL (Le Centre pour la Communication Scientifique Directe)arXiv (Cornell University)
- Partner nations
- United StatesGermany
In The Last Decade
Oksana Yakhnenko
8 papers receiving 138 citations
Peers
Comparison fields: 5 of 34
- Computer Vision and Pattern Recognition 94
- Artificial Intelligence 77
- Signal Processing 18
- Molecular Biology 15
- Media Technology 14
Countries citing papers authored by Oksana Yakhnenko
This map shows the geographic impact of Oksana Yakhnenko'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 Oksana Yakhnenko with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Oksana Yakhnenko more than expected).
Fields of papers citing papers by Oksana Yakhnenko
This network shows the impact of papers produced by Oksana Yakhnenko. 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 Oksana Yakhnenko. The network helps show where Oksana Yakhnenko may publish in the future.
Co-authorship network of co-authors of Oksana Yakhnenko
This figure shows the co-authorship network connecting the top 25 collaborators of Oksana Yakhnenko. A scholar is included among the top collaborators of Oksana Yakhnenko 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 Oksana Yakhnenko. Oksana Yakhnenko is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 16 | |
| 2 | 10 | |
| 3 | Region-Based Image Classification with a Latent SVM Model | 11 |
| 4 | 14 | |
| 5 | 16 | |
| 6 | 40 | |
| 7 | 5 | |
| 8 | 40 |
About Oksana Yakhnenko
Oksana Yakhnenko is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Health Information Management, having authored 8 papers that have together received 152 indexed citations. Recurring topics across this work include Image Retrieval and Classification Techniques (5 papers), Advanced Image and Video Retrieval Techniques (3 papers) and Bayesian Methods and Mixture Models (2 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (94 citations), Artificial Intelligence (77 citations) and Signal Processing (18 citations). Oksana Yakhnenko has collaborated with scholars based in United States and Germany. Frequent co-authors include Vasant Honavar, Adrian Silvescu, Nicolas Usunier, Jakob Verbeek, Cordelia Schmid, Jason Weston, Antoine Bordes, Balaji Krishnapuram and R. Bharat Rao. Their work appears in journals such as ACM SIGKDD Explorations Newsletter, HAL (Le Centre pour la Communication Scientifique Directe) and arXiv (Cornell University).
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.