Scalable Recognition with a Vocabulary Tree
- Authors
- D. NistérHenrik Stewénius
In The Last Decade
doi.org/10.1109/cvpr.2006.264 →Countries where authors are citing Scalable Recognition with a Vocabulary Tree
This map shows the geographic impact of Scalable Recognition with a Vocabulary Tree. 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 Scalable Recognition with a Vocabulary Tree with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Scalable Recognition with a Vocabulary Tree more than expected).
Fields of papers citing Scalable Recognition with a Vocabulary Tree
This network shows the impact of Scalable Recognition with a Vocabulary Tree. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the Scalable Recognition with a Vocabulary Tree.
About Scalable Recognition with a Vocabulary Tree
This paper, published in 2006, received 2.5k indexed citations . Written by D. Nistér and Henrik Stewénius covering the research area of Computer Vision and Pattern Recognition. It is primarily cited by scholars working on Computer Vision and Pattern Recognition (2.3k citations), Aerospace Engineering (1.0k citations) and Artificial Intelligence (282 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.
This paper is also available at doi.org/10.1109/cvpr.2006.264.