Part-based Object Recognition
Part-Based Object Recognition is a method in computer vision that identifies objects by decomposing them into constituent parts and modeling the spatial relationships among these parts, distinguishing it from holistic approaches that recognize objects based on global features.
375
Publications
62.6K
Citations
1K
Authors
375
Institutions
Publications per year
2017–2026
105
Parents
375
Learning the parts of objects by non-negative matrix factorization
Daniel D. Lee, H. Sebastian Seung · Nature · 1999 · 14.1K citations
Caltech-UCSD Birds-200-2011 Dataset
Catherine Wah · CaltechAUTHORS (California Institute of Technology) · 2024 · 3.9K citations · Full text
1–5 of 375
1K
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375
University of California, Berkeley
Berkeley, United States
16 h-index
33 publications
4.7K citations
University of Illinois Urbana-Champaign
Urbana, United States
13 h-index
35 publications
5.6K citations

Chinese Academy of Sciences
Beijing, China
11 h-index
39 publications
3.4K citations
Carnegie Mellon University
Pittsburgh, United States
11 h-index
26 publications
4.3K citations
University of California, Los Angeles
Los Angeles, United States
11 h-index
22 publications
3.3K citations

Peking University
Beijing, China
10 h-index
25 publications
2.6K citations
Microsoft (United States)
Redmond, United States
9 h-index
14 publications
3.4K citations
Stanford University
Stanford, United States
9 h-index
17 publications
2.6K citations
1–8 of 375
Repository · 25 publications · 793 citations
17
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TPAMI · 18 publications · 12.2K citations
16
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9 publications · 459 citations
9
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Conference · 6 publications · 364 citations
6
h-index
CVIU · 6 publications · 728 citations
6
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