Lucia Vadicamo
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 10%
- Signal Processing
- Information Systems
- Sociology and Political Science
- Co-authors
- Fabrizio FalchiGiuseppe AmatoFabio CarraraStefano CresciMaurizio TesconiFelice Dell’Orletta⋄Andrea CiminoRichard Connor
- Topics
- Advanced Image and Video Retrieval Techniques (15 papers)Multimodal Machine Learning Applications (8 papers)Data Management and Algorithms (7 papers)
- Partner nations
- ItalyUnited KingdomCzechia
In The Last Decade
Lucia Vadicamo
22 papers receiving 264 citations
Peers
Comparison fields: 5 of 59
- Computer Vision and Pattern Recognition 153
- Artificial Intelligence 138
- Signal Processing 31
- Information Systems 21
- Sociology and Political Science 13
Countries citing papers authored by Lucia Vadicamo
This map shows the geographic impact of Lucia Vadicamo'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 Lucia Vadicamo with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Lucia Vadicamo more than expected).
Fields of papers citing papers by Lucia Vadicamo
This network shows the impact of papers produced by Lucia Vadicamo. 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 Lucia Vadicamo. The network helps show where Lucia Vadicamo may publish in the future.
Co-authorship network of co-authors of Lucia Vadicamo
This figure shows the co-authorship network connecting the top 25 collaborators of Lucia Vadicamo. A scholar is included among the top collaborators of Lucia Vadicamo 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 Lucia Vadicamo. Lucia Vadicamo is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 7 | |
| 3 | 1 | |
| 4 | 16 | |
| 5 | 3 | |
| 6 | 2 | |
| 7 | 0 | |
| 8 | 5 | |
| 9 | 2 | |
| 10 | 24 | |
| 11 | 1 | |
| 12 | 26 | |
| 13 | 24 | |
| 14 | 0 | |
| 15 | 8 | |
| 16 | 86 | |
| 17 | Combining Fisher Vector and Convolutional Neural Networks for Image Retrieval. | 1 |
| 18 | 7 | |
| 19 | 20 | |
| 20 | Inscriptions visual recognition. A comparison of state-of-the-art object recognition approaches | 1 |
About Lucia Vadicamo
Lucia Vadicamo is a scholar working on Computer Vision and Pattern Recognition, Geography, Planning and Development and Signal Processing, having authored 26 papers that have together received 270 indexed citations. Recurring topics across this work include Advanced Image and Video Retrieval Techniques (15 papers), Multimodal Machine Learning Applications (8 papers) and Data Management and Algorithms (7 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (153 citations), Artificial Intelligence (138 citations) and Space and Planetary Science (5 citations). Lucia Vadicamo has collaborated with scholars based in Italy, United Kingdom and Czechia. Frequent co-authors include Fabrizio Falchi, Giuseppe Amato, Fabio Carrara, Stefano Cresci, Maurizio Tesconi, Felice Dell’Orletta⋄, Andrea Cimino, Richard Connor, Claudio Gennaro and Fabio Valerio Massoli. Their work appears in journals such as IEEE Access, ACM Computing Surveys and Future Generation Computer Systems.
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.