Fast Matching of Binary Features

By Marius Muja, David G. Lowe. 2012 Ninth Conference on Computer and Robot Vision, 2012

328 citations recorded by the source corpus.

Subfields: Computer Vision and Pattern Recognition, Information Systems, Signal Processing

View Paper: https://www.semanticscholar.org/paper/ad5ef08f29d367308fc27776d82e5ce9478388e0

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Vision and Pattern Recognition

1. Jingdong Wang
2. Dacheng Tao
3. Yi Yang
4. Luc Van Gool
5. Jingkuan Song
6. Nicu Sebe
7. Heng Tao Shen
8. Qi Tian

In Information Systems

1. Jingdong Wang
2. Heng Tao Shen
3. Jingkuan Song
4. Jianqiu Ji
5. Ting Zhang
6. Xiaotie Deng
7. Nicu Sebe

In Signal Processing

1. Jingdong Wang
2. Heng Tao Shen
3. Jingkuan Song
4. Ting Zhang
5. Lingxi Xie
6. Jianqiu Ji
7. David G. Lowe
8. Marius Muja

Citations in the corpus

• Scalable Nearest Neighbor Algorithms for High Dimensional Data (2014, background): “For a more detailed description of this algorithm the reader is encouraged to consult [47] and [52].”
• Fast Nearest Neighbor Search in the Hamming Space (2016, methodology): “FLANN [3] has been a well-know library for approximate nearest neighbor (ANN) search in the Euclidean space.”
• A Survey on Learning to Hash (2016, methodology): “Other research efforts include organizing the hash codes to avoid the exhaustive search with a data structure, such as a tree or a graph structure [104].”
• Hashing for Similarity Search: A Survey (2014, methodology): “[100] extends the FLANN algorithm [99] that is initially designed for ANN search over real-value vectors to search over binary vectors.”