A similarity learning approach to content-based image retrieval: application to digital mammography
By Issam El-Naqa, Yongyi Yang, N. Galatsanos, Robert M. Nishikawa, Miles N. Wernick. IEEE Transactions on Medical Imaging, 2004
312 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
View Paper: https://www.semanticscholar.org/paper/a696826649cc7f2f34efae5530788314f337c7bc
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Björn Ommer
- Artsiom Sanakoyeu
- Miguel Angel Bautista
- Ekaterina Tikhoncheva
- I. E. El Naqa
- Andrea Vedaldi
- João F. Henriques
- Irfan Essa
In Computer Vision and Pattern Recognition
- Björn Ommer
- Artsiom Sanakoyeu
- Miguel Angel Bautista
- Ekaterina Tikhoncheva
- I. E. El Naqa
- Andrea Vedaldi
- Luc Van Gool
- Robin Rombach
Citations in the corpus
- CliqueCNN: Deep Unsupervised Exemplar Learning (2016, methodology): “Among the many approaches for similarity learning, supervised techniques have been particularly popular in the vision community, leading to the formulation as a ranking [23], regression [6], and classification [17] task.”
- The role of quantitative PET in predicting cancer treatment outcomes (2014, methodology): “These methods have been applied successfully in many diverse areas including outcome prediction [40, 83–86].”