Image Retrieval and Classification Techniques
Image Retrieval and Classification Techniques is a research topic within Computer Vision and Pattern Recognition. Science Explorer counts 49k research works in it since 1956. 16.7% of them reached the world's top 10% most cited for their field and year.
This cluster of papers focuses on shape matching, object recognition, and content-based image retrieval using techniques such as local binary patterns, feature descriptors, and rotation-invariant methods. It also explores the application of these methods in medical imaging, semantic relevance modeling, and machine learning for image annotation.
- Shape Matching
- Content-Based Image Retrieval
- Texture Classification
- Local Binary Patterns
- Feature Descriptors
- Semantic Relevance
- Rotation Invariant
- Medical Applications
- Machine Learning
- Image Annotation
- Research works
- 49k fractional, since 1956
- In the world top 10%
- 8.2k per year above
- Top-10% rate
- 16.7% share of its works in the world top 10%
- Growth, 2013–17 → 2018–22
- -26% the tick is no change
Which countries lead Image Retrieval and Classification Techniques research?
By volume, China and India publish the most (2.6k and 814 works in 2022–2025).
By volume, 2022–2025
- 1 China 2.6k works
- 2 India 814 works
- 3 United States 491 works
- 4 Japan 151 works
- 5 South Korea 133 works
- 6 United Kingdom 125 works
- 7 France 122 works
- 8 Germany 122 works
- 9 Indonesia 97 works
- 10 Italy 94 works
How concentrated that is
The same countries as shares of everything the list above accounts for. A node where two countries do two thirds of the work and one spread evenly across twelve read alike as a ranking and not at all alike here.
Shares of the rows listed above, not of the whole node.
Which institutions lead Image Retrieval and Classification Techniques research?
By volume in 2022–2025, Xidian University publishes the most Image Retrieval and Classification Techniques research, followed by University of Science and Technology of China and Tsinghua University.
By volume, 2022–2025
- 1 Xidian UniversityChina 50 works
- 2 University of Science and Technology of ChinaChina 40 works
- 3 Tsinghua UniversityChina 35 works
- 4 Wuhan UniversityChina 35 works
- 5 Chinese Academy of SciencesChina 34 works
- 6 Zhejiang UniversityChina 32 works
- 7 Northwestern Polytechnical UniversityChina 29 works
- 8 University of Electronic Science and Technology of ChinaChina 29 works
- 9 Shanghai Jiao Tong UniversityChina 28 works
- 10 Beijing University of Posts and TelecommunicationsChina 27 works
Who are the leading researchers in Image Retrieval and Classification Techniques?
The most-cited researchers publishing on Image Retrieval and Classification Techniques include Andrew Zisserman, Li Fei-Fei and Serge Belongie.
- 1 Andrew Zisserman United Kingdom 25k citations
- 2 Li Fei-Fei United States 17k citations
- 3 Serge Belongie United States 14k citations
- 4 Xiaogang Wang Russia 13k citations
- 5 Jitendra Malik United States 12k citations
- 6 Pietro Perona United States 11k citations
- 7 Alexander C. Berg United States 9.9k citations
- 8 Wei Liu China 9.7k citations
- 9 Olga Russakovsky United States 8.9k citations
- 10 Luc Van Gool Switzerland 8.5k citations
Ranked by citations received across their whole record, among researchers with at least three works on this topic.
Where is Image Retrieval and Classification Techniques research done?
The largest centres of Image Retrieval and Classification Techniques research in 2022–2025 are Beijing (China), Xi'an (China), Shanghai (China) and Wuhan (China). Among places with at least 20 works in it, it is an unusually large share of all research in Guilin.
Largest cities, 2022–2025
Where it is the local speciality
- GuilinCN · 25.9 works5.1×
Location quotient: how much more of its research is in Image Retrieval and Classification Techniques than the world average.
Where is the best place to study Image Retrieval and Classification Techniques?
Among universities, judged by research, Xidian University, Wuhan University and Chongqing University of Posts and Telecommunications score highest, combining excellence, specialisation, size, growth and international reach. Research strength is one signal when choosing where to study; it does not measure teaching.
One dot per university in the table below. The upper left is the interesting corner: small places doing unusually strong work.
| # | University | Score | Top 10% | Specialisation | Works | Growth |
|---|---|---|---|---|---|---|
| 1 | Xidian UniversityChina | 70.5 | 28.0% | 11.3× | 50 | +31.2% |
| 2 | Wuhan UniversityChina | 51.0 | 30.8% | 4.7× | 35 | -14.7% |
| 3 | Chongqing University of Posts and TelecommunicationsChina | 51.0 | 14.2% | 13.3× | 23 | +9.6% |
| 4 | Renmin University of ChinaChina | 50.4 | 39.7% | 5.8× | 12 | +13.9% |
| 5 | Beijing University of Posts and TelecommunicationsChina | 50.0 | 22.4% | 7.2× | 27 | +4.6% |
| 6 | Xinjiang UniversityChina | 50.0 | 21.3% | 8.3× | 22 | +19.0% |
| 7 | Hong Kong University of Science and TechnologyHong Kong | 48.0 | 34.7% | 4.7× | 11 | -24.4% |
| 8 | China University of GeosciencesChina | 47.9 | 33.0% | 5.1× | 14 | +34.7% |
| 9 | Hunan UniversityChina | 47.2 | 28.2% | 3.5× | 15 | +152.9% |
| 10 | Anhui UniversityChina | 47.1 | 24.3% | 7.2× | 20 | -17.3% |
Universities only. Score blends excellence (30%), specialisation (25%), size (20%), growth (15%) and international reach (10%), 2015–2022; growth compares 2010–14 with 2015–19.
Is Image Retrieval and Classification Techniques research growing?
Output in 2018–2022 was 26% lower than in 2013–2017, peaking in 2010. The fastest-growing topics are Image Retrieval and Classification Techniques.
The same series as a ribbon — one cell per year, darker for more. The line above answers how much; this answers when.
Which topics inside it are moving
Growth and decline on one axis around a shared zero. Two lists side by side hide the thing that matters: whether the growth dwarfs the decline, or the other way round.